CN114579758A

CN114579758A - Method, device, terminal and storage medium for constructing OWL (ontology of Web language) system by combining RPA (resilient packet Access) and AI (Artificial Intelligence of origin)

Info

Publication number: CN114579758A
Application number: CN202210173040.0A
Authority: CN
Inventors: 李艳丹; 张海雷; 门波; 唐梓毅; 黄伟; 刘崴
Original assignee: Laiye Technology Beijing Co Ltd
Current assignee: Laiye Technology Beijing Co Ltd
Priority date: 2022-02-24
Filing date: 2022-02-24
Publication date: 2022-06-03

Abstract

The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for constructing an OWL system in combination with an RPA and an AI. The OWL system construction method comprises the following steps: acquiring a first entity set input aiming at an initial web ontology language (OWL) system; acquiring an attribute information set corresponding to at least one entity in the first entity set, wherein the attribute information set comprises an object attribute information subset, a data attribute information subset and a relation attribute information subset; and constructing the initial web ontology language OWL system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset. By the adoption of the method and the device, the accuracy of the OWL system construction can be improved, and the use experience of a user is further improved.

Description

Method, device, terminal and storage medium for constructing OWL (ontology of Web language) system by combining RPA (resilient packet Access) and AI (Artificial Intelligence of origin)

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for constructing an OWL system in combination with an RPA and an AI.

Background

Robot Process Automation (RPA) is a Process task that simulates human operations on a computer through specific robot software and automatically executes according to rules.

Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence.

With the advent of the big data age, information carried on networks is becoming more and more abundant. For more and more information on the network, a network Ontology Language (OWL) system is developed. OWL may assist users in managing information carried on the network. However, in the related art, OWL has a single function, which further degrades the user experience.

Disclosure of Invention

The embodiment of the application provides a method, a device, a terminal and a storage medium for constructing an OWL system by combining RPA and AI, so as to solve the problems in the related art, and the technical scheme is as follows:

in a first aspect, an embodiment of the present application provides a method for constructing an OWL system in combination with an RPA and an AI, including:

acquiring a first entity set input aiming at an initial web ontology language (OWL) system;

acquiring an attribute information set corresponding to at least one entity in a first entity set, wherein the attribute information set comprises an object attribute information subset, a data attribute information subset and a relation attribute information subset;

and constructing an initial web ontology language (OWL) system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset.

In one embodiment, after constructing the initial web ontology language OWL system based on the object attribute information subset, the data attribute information subset, and the relationship attribute information subset, the method further includes:

acquiring a bid document in a target document format through a Robot Process Automation (RPA) system, and identifying the bid document by adopting an Optical Character Recognition (OCR) technology to obtain a structured bid document;

acquiring a target object attribute information subset and a target relation attribute information subset corresponding to an initial web ontology language (OWL) system from a structured bidding document;

and constructing a target OWL system based on the target object attribute information subset and the target relation attribute information subset.

In one embodiment, acquiring a first entity set input for an initial web ontology language OWL system includes:

acquiring a construction instruction input by aiming at an initial web ontology language (OWL) system;

displaying an entity set selection interface in response to the construction instruction;

acquiring a selection instruction input aiming at a second entity set in the entity set selection interface;

and responding to a selection instruction, and acquiring a first entity set input aiming at the initial web ontology language OWL system.

displaying an entity set input interface in response to the construction instruction;

a first entity set input in an entity set input interface is obtained.

In one embodiment, the acquiring the attribute information set corresponding to at least one entity in the first entity set includes:

acquiring an attribute identifier subset corresponding to any entity in a first entity set;

acquiring an attribute type subset corresponding to any entity;

acquiring an attribute information subset corresponding to any entity based on the attribute identification subset and the attribute type subset;

and traversing the first entity set to obtain an attribute information set corresponding to at least one entity.

In one embodiment, the method further comprises:

acquiring an entity type set corresponding to each entity in a first entity set;

and constructing an initial web ontology language (OWL) system corresponding to the entity type set.

In a second aspect, an embodiment of the present application provides an OWL system constructing apparatus combining RPA and AI, including:

the set acquisition unit is used for acquiring a first entity set input by aiming at an initial web ontology language (OWL) system;

the information acquisition unit is used for acquiring an attribute information set corresponding to at least one entity in the first entity set, wherein the attribute information set comprises an object attribute information subset, a data attribute information subset and a relation attribute information subset;

and the system construction unit is used for constructing an initial web ontology language OWL system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset.

In one embodiment, the apparatus further comprises a document obtaining unit, an attribute obtaining unit, and an OWL constructing unit, configured to, after constructing the initial web ontology language OWL system based on the object attribute information subset, the data attribute information subset, and the relationship attribute information subset:

the system comprises a document acquisition unit, a document processing unit and a document processing unit, wherein the document acquisition unit is used for acquiring a bid document in a target document format through a Robot Process Automation (RPA) system, and identifying the bid document by adopting an Optical Character Recognition (OCR) technology to obtain a structured bid document;

the attribute acquisition unit is used for acquiring a target object attribute information subset and a target relation attribute information subset corresponding to an initial web ontology language (OWL) system in the structured bid-inviting and bidding document;

and the OWL constructing unit is used for constructing a target OWL system based on the target object attribute information subset and the target relation attribute information subset.

In one embodiment, the set acquiring unit includes a construction instruction acquiring subunit, a selection interface showing subunit, a selection instruction acquiring subunit, and an instruction response subunit, and when the set acquiring unit is configured to acquire a first entity set input for the initial web ontology language OWL system:

the building instruction acquisition subunit is used for acquiring a building instruction input by aiming at the initial web ontology language OWL system;

the selection interface display subunit is used for responding to the construction instruction and displaying the entity set selection interface;

the selection instruction acquisition subunit is used for acquiring a selection instruction input by aiming at a second entity set in the entity set selection interface;

and the instruction response subunit is used for responding to the selection instruction and acquiring the first entity set input by the initial web ontology language OWL system.

In one embodiment, the set acquiring unit further includes an instruction acquiring subunit, an interface displaying subunit, and an entity set acquiring subunit, where the set acquiring unit is configured to, when acquiring a first entity set input for the initial web ontology language OWL system:

the instruction acquisition subunit is used for acquiring a construction instruction input by aiming at the initial web ontology language OWL system;

the interface display subunit is used for responding to the construction instruction and displaying the entity set input interface;

and the entity set acquisition subunit is used for acquiring the first entity set input in the entity set input interface.

In one embodiment, the attribute information set includes an attribute identifier subset and an attribute type subset, the information obtaining unit includes an identifier obtaining subunit, a type obtaining subunit, a subset obtaining subunit, and a set traversal subunit, and the information obtaining unit is configured to obtain an attribute information set corresponding to at least one entity in the first entity set;

the identifier obtaining subunit is configured to obtain an attribute identifier subset corresponding to any entity in the first entity set;

the type obtaining subunit is used for obtaining an attribute type subset corresponding to any entity;

the subset acquisition subunit is used for acquiring the attribute information subset corresponding to any entity based on the attribute identifier subset and the attribute type subset;

and the set traversing subunit is used for traversing the first entity set and acquiring the attribute information set corresponding to at least one entity.

In one embodiment, the apparatus further comprises:

the type set acquisition unit is used for acquiring an entity type set corresponding to each entity in the first entity set;

and the system construction unit is also used for constructing an initial web ontology language (OWL) system corresponding to the entity type set.

In a third aspect, an embodiment of the present application provides a terminal combining an RPA and an AI, where the terminal includes: a memory and a processor. Wherein the memory and the processor are in communication with each other via an internal connection path, the memory is configured to store instructions, the processor is configured to execute the instructions stored by the memory, and the processor is configured to perform the method of any of the above aspects when the processor executes the instructions stored by the memory.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and when the computer program runs on a computer, the method in any one of the above-mentioned aspects is executed.

The advantages or beneficial effects in the above technical solution at least include:

acquiring a first entity set input aiming at an initial web ontology language (OWL) system; acquiring an object attribute information subset corresponding to at least one entity in a first entity set, acquiring a data attribute information subset corresponding to at least one entity in the first entity set, and acquiring a relationship attribute information subset corresponding to at least one entity; and constructing an initial web ontology language (OWL) system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset. The user can construct the required OWL ontology by selecting the first set of entities entered. By designing the object attribute information, the data attribute information and the relationship attribute information and constructing the OWL based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset, the accuracy of constructing the OWL system can be improved, and the use experience of a user can be further improved.

The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will be readily apparent by reference to the drawings and following detailed description.

Drawings

In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.

FIG. 1 is a background diagram of a method for constructing an OWL system in combination with RPA and AI according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram illustrating a background architecture of a method for constructing an OWL system in combination with RPA and AI according to an embodiment of the present application;

FIG. 3 is a flow chart of a method for constructing an OWL system in combination with RPA and AI according to an embodiment of the present application;

FIG. 4 is a flow chart of a method for constructing an OWL system combining RPA and AI according to an embodiment of the present application;

FIG. 5 is a schematic illustration of a presentation of an entity collection selection interface according to an embodiment of the present application;

FIG. 6 is a diagram illustrating a structure of a subset of data attribute information according to an embodiment of the present application;

FIG. 7 is a diagram illustrating a structure of a subset of relationship attribute information according to an embodiment of the present application;

FIG. 8 shows a presentation diagram of an initial OWL according to an embodiment of the present application;

FIG. 9 illustrates a flowchart of document format conversion, in accordance with one embodiment of the present application;

FIG. 10 is a flow diagram illustrating a qualification expression solution according to an embodiment of the present application;

FIG. 11 is a schematic illustration of a presentation of an entity collection input interface according to an embodiment of the present application;

fig. 12 is a schematic structural diagram of an OWL system construction device combining RPA and AI according to an embodiment of the present application;

fig. 13 is a schematic structural diagram of an OWL system constructing apparatus according to an embodiment of the present application, which combines RPA and AI;

fig. 14 is a schematic structural diagram of an OWL system constructing apparatus according to an embodiment of the present application, which combines RPA and AI;

fig. 15 is a schematic structural diagram of an OWL system constructing apparatus according to an embodiment of the present application, which combines RPA and AI;

fig. 16 is a schematic structural diagram of an OWL system constructing apparatus according to an embodiment of the present application, which combines RPA and AI;

fig. 17 is a schematic structural diagram of an OWL system constructing apparatus according to an embodiment of the present application, which combines RPA and AI;

fig. 18 shows a block diagram of a terminal according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.

In the description of the present application, the term "plurality" means two or more.

In the description of the present application, the term "RPA" refers to the automatic execution of flow tasks by rules, by a specific "robot software", simulating the human operation on a computer. Professional and comprehensive process automation solutions can be provided for enterprises and individuals. The RPA robot can intelligently understand the existing application of the government and the enterprise through a user use interface, automatically and regularly based conventional operations such as automatically and repeatedly reading mails, reading Office components, operating databases, webpages and client software, collecting data, performing complicated calculation, generating files and reports in large batch, and completing boring work such as file inspection. The input of labor cost can be greatly reduced, the existing office efficiency is effectively improved, and the work is accurately, stably and quickly finished.

In the description of the present application, the term "knowledge graph" refers to a structured semantic knowledge base for rapidly describing concepts and their interrelations in the physical world, and by reducing the data granularity from the document level to the data level, a large amount of knowledge can be aggregated, thereby enabling rapid response and reasoning of knowledge.

In the description of the present application, the term "entity" refers to things that can be distinguished from each other, the entity being objectively present. May refer to a collection of something. The system can be concrete personnel, and also can be abstract concepts and connections.

In the description of the present application, the term "object attribute information" refers to an entity name or attribute name that abstracts and scores a homogeneous entity. Object attribute information includes, but is not limited to, people, institutions, time, posts, accounts, and the like.

In the description of the present application, the term "data attribute information" refers to attribute information corresponding to an entity itself. For example, attribute information corresponding to the entity "person M" includes, but is not limited to, name M1, past name M2, age M3, and the like.

In the description of the present application, the term "data attribute identification" refers to identification information corresponding to attribute information in data attribute information. For example, the identification information corresponding to the "name M1" is "name", the identification information corresponding to the past name M2 is "past name", and the identification information corresponding to the "age M3" is "age".

In the description of the present application, the term "data attribute type" refers to object attribute information corresponding to an entity itself to which the data attribute information corresponds. For example, the data attribute type corresponding to the entity "person M" is "person"; the data attribute type corresponding to the entity 'organization N' is 'organization'.

In the description of the present application, the term "relationship attribute information" refers to mutual relationship information between entities. For example, entity "person M" is a legal of entity "organization N1"; the entity "person M" is a primary member of the entity "organization N2".

In the description of the present application, the term "relationship attribute identification" refers to identification information corresponding to attribute information in relationship attribute information.

In the description of the present application, the term "relationship attribute type" refers to object attribute information corresponding to an entity itself corresponding to relationship attribute information.

In the description of the present application, the term "NLP" refers to Natural Language Processing (Natural Language Processing), which is an important direction in the fields of computer science and artificial intelligence. Specifically, the language is used as an object, and a subject of analyzing, understanding and processing natural language is realized by using computer technology, namely, a computer is used as a powerful tool for language research, language information is quantitatively researched under the support of the computer, and language description which can be commonly used between a person and the computer is provided. The method comprises a Natural Language Understanding (NLU) part and a Natural Language Generation (NLG) part. NLP is a typical edge interdisciplinary, which relates to linguistic science, computer science, mathematics, cognition, logic, etc., and studies various theories and methods that enable efficient communication between humans and computers using natural language. The process of processing Natural Language with a computer is also called Natural Language Understanding (NLU), Human Language Technology (HLT), Computational Linguistics (Hl), Quantitative Linguistics (Quantitative Linguistics), and Mathematical Linguistics (Mathematical Linguistics), respectively, at different times or with different emphasis points.

In the description of the present application, the term "OCR" refers to Optical Character Recognition (Optical Character Recognition), and specifically refers to a process in which an electronic device examines a Character printed on paper, determines its shape by detecting dark and light patterns, and then translates the shape into a computer text by a Character Recognition method; the method is characterized in that characters in a paper document are converted into an image file with a black-white dot matrix in an optical mode aiming at print characters, and the characters in the image are converted into a text format through recognition software for further editing and processing by word processing software.

In the description of the present application, the term "bid document" includes bid documents and bid documents. The bidding document refers to an offer invitation document which is sent by the bidder to the potential bidder and informs the potential bidder of information such as project requirements, bidding activity rules, contract conditions and the like, and is a main basis of project bidding activity. A bid document refers to a responsive document that a bidder should call for documentation.

In the description of the present application, the term "structured bid document" refers to a document that consists of logical structures, such as titles, sections, paragraphs, etc. The structured bid document does not refer specifically to a fixed document. For example, when the content of a bid document changes, the structured bid document may also change accordingly. When the document format of the bid document changes, the structured bid document may also change accordingly.

In the description of the present application, the term "OWL" is a component of semantic Web activity. The network ontology language is a core voice tool in the semantic network development process. The OWL system has the characteristics of strict structure, concise language and intuitive expression, and is widely applied. The web ontology language is intended to provide a language that can be used to describe those classes and relationships between them that are inherent in web documents and applications.

With the development of scientific technology, terminal technology is mature day by day, and convenience of production and life of users is improved. In a terminal application scene, a user can construct a web ontology language OWL system through a terminal.

According to some embodiments, fig. 1 is a schematic background diagram illustrating a method for constructing an OWL system combining RPA and AI according to an embodiment of the present application. As shown in fig. 1, a user may click on a system building application of a terminal, and when the terminal detects that the user clicks on the system building application, the terminal may present a system building interface. The user can input the information set needing to be managed based on the system construction interface. The terminal may construct a corresponding OWL based on the information set.

In some embodiments, an OWL constructed by the terminal can only manage an input information set, and the function is single, thereby affecting the use experience of the user.

According to some embodiments, fig. 2 is a schematic diagram illustrating a background architecture of an OWL system construction method combining RPA and AI according to an embodiment of the present application. As shown in fig. 2, the terminal 11 may upload a set of information that needs to be managed, which is input by a user, to the server 13 through the network 12. When the server 13 receives the information set, the server 13 may send the constructed OWL to the terminal 11 through the network 12, and when the terminal receives the OWL sent by the server 13, the terminal may display an OWL presentation interface, and a user may refer to and edit the input information set based on the OWL presentation interface.

It is readily understood that the terminal includes, but is not limited to: wearable devices, handheld devices, personal computers, tablet computers, in-vehicle devices, smart phones, computing devices or other processing devices connected to a wireless modem, and the like. The terminal devices in different networks may be called different names, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5th generation mobile network or future evolution network, and the like. The terminal can be installed with an operating system, which is an operating system capable of running in the terminal, is a program for managing and controlling terminal hardware and terminal applications, and is an indispensable system application in the terminal. The operating system includes, but is not limited to, Android, IOS, Windows Phone (WP), and Ubuntu mobile operating system.

According to some embodiments, the terminal 11 may be connected to the server 13 via the network 12. The network 12 is used to provide a communication link between the terminal 11 and the server 13. Network 12 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few. It should be understood that the number of terminals 11, networks 12 and servers 13 in fig. 2 is merely illustrative. There may be any number of terminals, networks and servers, as desired for the reality. For example, the server 13 may be a server cluster composed of a plurality of servers. The user may use the terminal 11 to interact with the server 13 through the network 12 to perform OWL construction and the like.

These and other aspects of embodiments of the present application will be apparent from and elucidated with reference to the following description and drawings. In the description and drawings, particular embodiments of the application are disclosed in detail as being indicative of some of the ways in which the principles of the embodiments of the application may be practiced, but it is understood that the embodiments of the application are not limited correspondingly in scope. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.

The method for constructing the OWL system combining the RPA and the AI according to the embodiment of the application is described in the following with the accompanying drawings.

Fig. 3 is a flowchart illustrating a method for constructing an OWL system combining RPA and AI according to an embodiment of the present application, where as shown in fig. 3, the method may include the following steps:

step S101: acquiring a first entity set input aiming at an initial web ontology language (OWL) system;

according to some embodiments, the first entity refers to an entity that the terminal needs to use when constructing the initial OWL. The first entity is not specifically designated as a fixed entity. The first entity includes, but is not limited to, a person, an organization, an event, a post, an account, and the like. The first entity set refers to a set formed by gathering at least one first entity. The first set of entities does not refer to a fixed set. For example, when the number of first entities changes, the first set of entities may also change. When the content of a first entity changes, the first set of entities may also change.

It is easily understood that when a terminal constructs an initial OWL, the terminal may acquire a first set of entities entered for the initial OWL.

Step S102: acquiring an attribute information set corresponding to at least one entity in a first entity set;

according to some embodiments, the attribute information set refers to a set aggregated by at least one attribute information. The set of attribute information includes an object attribute information subset, a data attribute information subset, and a relationship attribute information subset. The attribute information set does not refer to a fixed information set. For example, when any attribute information in the attribute information set changes, the attribute information set may also change correspondingly.

It is easily understood that the object attribute information subset refers to a set into which at least one object attribute information is aggregated. The subset of object attribute information does not refer to a fixed set. For example, when the first set of entities changes, the subset of object attribute information may also change. When the object attribute information changes, the subset of object attribute information may also change.

In some embodiments, a subset of data attribute information refers to a set of at least one data attribute information. The subset of data attribute information does not refer to a fixed set. For example, when the first set of entities changes, the subset of data attribute information may also change. When the data attribute information changes, the subset of data attribute information may also change.

In some embodiments, a relationship attribute information subset refers to a set of at least one relationship attribute information aggregate. The subset of relationship attribute information does not refer to a fixed set. For example, when the first set of entities changes, the subset of relationship attribute information may also change. When the relationship attribute information changes, the subset of relationship attribute information may also change.

It is easy to understand that, when the terminal acquires the first entity set, the terminal may acquire the subset of the object attribute information corresponding to at least one entity in the first entity set. The terminal may also obtain a data attribute information subset corresponding to at least one entity in the first entity set. The terminal may further obtain a relationship attribute information subset corresponding to at least one entity, that is, the terminal may obtain an attribute information set corresponding to at least one entity in the first entity set.

Step S103: and constructing an initial web ontology language (OWL) system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset.

According to some embodiments, the initial web ontology language OWL system refers to OWLs constructed based on attribute information sets. The initial OWL is not specific to a fixed OWL. For example, when a subset of object attribute information changes, the initial OWL also changes. When a change occurs in the data attribute information subset, the initial OWL also changes. When a subset of relationship attribute information changes, the initial OWL also changes.

It is easy to understand that when the terminal acquires the attribute information set, that is, the terminal acquires the object attribute information subset, the data attribute information subset, and the relationship attribute information subset, the terminal may construct an initial OWL based on the object attribute information subset, the data attribute information subset, and the relationship attribute information subset.

In the embodiment of the application, a first entity set input aiming at an initial web ontology language OWL system is obtained; acquiring an object attribute information subset corresponding to at least one entity in a first entity set, acquiring a data attribute information subset corresponding to at least one entity in the first entity set, and acquiring a relationship attribute information subset corresponding to at least one entity; and constructing an initial web ontology language (OWL) system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset. The user can construct the required OWL ontology by selecting the first set of entities entered. By designing the object attribute information, the data attribute information and the relationship attribute information and constructing the OWL based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset, the accuracy of constructing the OWL system can be improved, the application scenes of the OWL are enriched, and the use experience of a user can be improved.

Fig. 4 is a flowchart illustrating a method for constructing an OWL system combining RPA and AI according to an embodiment of the present application, where as shown in fig. 4, the method may include the following steps:

step S201: acquiring a construction instruction input by aiming at an initial network ontology language (OWL) system;

according to some embodiments, the build instruction refers to an instruction issued when a user needs to obtain an initial OWL. The build instruction does not refer specifically to a fixed instruction. The build instructions include, but are not limited to, click build instructions, voice build instructions, and the like. For example, a user may click a key corresponding to the construction instruction based on a system construction interface displayed by the terminal, and at this time, the terminal may obtain the construction instruction input for the initial OWL. Or, when the user speaks the voice information corresponding to the construction instruction, the terminal may also obtain the construction instruction input for the initial OWL.

It is easy to understand that, when a user needs to construct an initial OWL, the user may issue a construction instruction for the initial OWL, and then, the terminal may obtain the construction instruction input for the initial OWL.

Step S202: displaying an entity set selection interface in response to the construction instruction;

according to some embodiments, the entity set selection interface refers to an interface displayed by the terminal when the user selects a first entity required to be used when the user creates an initial OWL through the terminal. The entity set selection interface does not refer to a fixed interface. The interface includes, but is not limited to, a full screen interface, a landscape screen interface, a half-window interface, a floating window interface, and the like.

In some embodiments, floating a window refers to a window that can float over a single page or multiple pages. When the terminal needs to move the floating window, the terminal can acquire a moving instruction for the floating window, and the floating window is moved to a display position corresponding to the moving instruction in response to the moving instruction. When the terminal needs to adjust the floating window, an adjusting instruction aiming at the floating window is obtained; and responding to the adjusting instruction, and adjusting the size of the floating window to the display size corresponding to the adjusting instruction.

It is easy to understand that when the terminal acquires the building instruction input for the initial OWL, the terminal may present the entity set selection interface in response to the building instruction.

Step S203: acquiring a selection instruction input aiming at a second entity set in the entity set selection interface;

according to some embodiments, the second set of entities refers to a set of entities presented in the entity set selection interface. The second set of entities does not refer to a fixed set. For example, when the content of an entity in a second set of entities changes, the second set of entities may also change. When the number of entities in the second set of entities changes, the second set of entities may also change.

In some embodiments, when the entity set selection interface cannot show the entity information included in all the second entity sets, the terminal may set a scroll bar, and show the entity information included in all the second entity sets through the scroll bar. The entity information contained in all of the second entity sets may also be presented by adjusting the size of the entity set selection interface.

In some embodiments, the entity set selection interface further includes an editing function. When the user needs to modify the second set of entities, the user may issue editing instructions for the second set of entities. When the terminal receives the editing instruction, the terminal can display the entity editing sub-interface. The user may edit, add entity information, and prune entity information for entities in the second set of entities based on the entity edit sub-interface.

According to some embodiments, the selection instruction refers to an instruction issued when a user selects an entity to be used. The select instruction does not specifically refer to a fixed instruction. The selection instruction includes, but is not limited to, a click selection instruction, a voice selection instruction, and the like. For example, a user may click a key corresponding to the construction instruction based on an entity set selection interface displayed by the terminal, and at this time, the terminal may obtain a selection instruction input for a second entity set in the entity set selection interface. Or, when the user speaks the voice information corresponding to the construction instruction, the terminal may also select an instruction input for the second entity set in the entity set selection interface.

It is easy to understand that, when the terminal presents the entity set selection interface, the user may select, based on the entity set selection interface, entity information that needs to be used in constructing the initial OWL from the second entity set, and send a selection instruction. Furthermore, the terminal can obtain a selection instruction input aiming at a second entity set in the entity set selection interface.

Step S204: responding to a selection instruction, and acquiring a first entity set input by aiming at an initial network ontology language OWL system;

in some embodiments, fig. 5 shows a schematic illustration of an entity set selection interface according to an embodiment of the present application. As shown in fig. 5, the user can select the entities to be used based on the entity set selection interface: a1, B2 and C1. When the user does not find the entity needed to be used in the entity set selection interface, the user can click an 'edit' button to add the entity needed to be used in the second entity set. When the user finishes selecting all the entities needed to be used, the user can click the 'finish' key to send out a selection instruction. When the terminal acquires the selection instruction, the terminal may respond to the selection instruction to acquire an entity including: a first set of entities a1, B2, C1.

It is easy to understand that when the terminal acquires a selection instruction input for a second entity set in the entity set selection interface, the terminal may acquire a first entity set input for the initial web ontology language OWL system in response to the selection instruction.

Step S205: acquiring an attribute information set corresponding to at least one entity in a first entity set;

according to some embodiments, the attribute information set includes an attribute identifier subset and an attribute type subset, when the terminal acquires the attribute information set corresponding to at least one entity in the first entity set, the terminal may acquire the attribute identifier subset corresponding to any entity in the first entity set, and the terminal may acquire the attribute type subset corresponding to any entity.

According to some embodiments, the subset of object attribute information includes a subset of object attribute identifications and a subset of object attribute types. When the terminal acquires the object attribute information subset corresponding to at least one entity in the first entity set, the terminal can acquire the object attribute identifier subset corresponding to any entity; and acquiring an object attribute type subset corresponding to any entity, and acquiring an object attribute information subset corresponding to any entity based on the attribute identification subset and the attribute type subset.

According to some embodiments, the subset of data attribute information includes a subset of data attribute identifications and a subset of data attribute types. When the terminal acquires the data attribute information subset corresponding to at least one entity in the first entity set, the terminal can acquire the data attribute identifier subset corresponding to any entity; and acquiring a data attribute type subset corresponding to any entity.

In some embodiments, the data attribute identifier subset refers to a set formed by aggregating at least one data attribute identifier corresponding to the data attribute information subset. The subset of data attribute identifications does not refer specifically to a fixed set. For example, when a change occurs in the subset of data attribute information, the subset of data attribute identifications may also change. When a change occurs in the data attribute identifications, the subset of data attribute identifications may also change.

In some embodiments, the data attribute identifier subset refers to a set formed by aggregating at least one data attribute identifier corresponding to any entity in the data attribute identifier subset. The data attribute identification subset does not refer specifically to a fixed set. For example, when a change occurs to a subset of data attribute identifications, the subset of data attribute identifications may also change. When a data attribute identity changes, the subset of data attribute identities may also change.

In some embodiments, the data attribute type subset refers to a set formed by aggregating at least one data attribute type corresponding to the data attribute information subset. The subset of data attribute types does not refer specifically to a fixed set. For example, when a subset of data attribute information changes, the subset of data attribute types may also change. When a data attribute type changes, the subset of data attribute types may also change.

In some embodiments, the data attribute type subset refers to a set formed by aggregating at least one data attribute type corresponding to any entity in the data attribute type subset. The subset of data attribute types does not refer specifically to a fixed set. For example, when a subset of data attribute types changes, the subset of data attribute types may also change. When a data attribute type changes, the subset of data attribute types may also change.

According to some embodiments, when the terminal acquires the data attribute identifier subset and the data attribute type subset, the terminal may acquire the data attribute information subset corresponding to any entity based on the data attribute identifier subset and the data attribute type subset. The terminal can obtain the data attribute information subset corresponding to at least one entity by traversing the first entity set. Therefore, the terminal can improve the accuracy of constructing the OWL system by acquiring the data attribute information subset, and further improve the use experience of the user.

In some embodiments, the data attribute information subset refers to a set into which data attribute information corresponding to any entity is aggregated. The subset of data attribute information does not refer to a fixed information. For example, when an entity corresponding to the data attribute information subset changes, the data attribute information subset may also change. When the data attribute information changes, the subset of data attribute information may also change.

For example, fig. 6 shows a schematic structural diagram of a data attribute information subset according to an embodiment of the present application. As shown in fig. 6, the terminal may present a subset of data attribute information corresponding to the entity "person a 1" in the display interface: name a1, great name a2, age a3, gender woman, hobby XXX.

For example, when the first entity set includes entities a1, B2, and C1, the terminal may traverse the first entity set, and sequentially acquire the data attribute information subset D1 corresponding to the entity a1, the data attribute information subset D2 corresponding to the entity B2, and the data attribute information subset D3 corresponding to the entity C1. Further, the terminal may obtain a subset of data attribute information including D1, D2, D3.

According to some embodiments, the relationship attribute information subset includes a relationship attribute identification subset and a relationship attribute type subset. When the terminal acquires the relationship attribute information subset corresponding to at least one entity, the terminal can acquire the relationship attribute identifier subset corresponding to any entity in the first entity set; acquiring a relation attribute type subset corresponding to any entity;

in some embodiments, the relationship attribute identifier subset refers to a set formed by aggregating at least one relationship attribute identifier corresponding to the relationship attribute information subset. The relationship attribute identification subset does not refer specifically to a fixed set. For example, when a subset of relationship attribute information changes, the subset of relationship attribute identifications may also change. When a relationship attribute identification changes, the subset of relationship attribute identifications may also change.

In some embodiments, the relationship attribute identifier subset refers to a set formed by aggregating at least one relationship attribute identifier corresponding to any entity in the relationship attribute identifier subset. The relationship attribute identification subset does not refer specifically to a fixed set. For example, when a subset of relationship attribute identifications changes, the subset of relationship attribute identifications may also change. When a relationship attribute identification changes, the subset of relationship attribute identifications may also change.

In some embodiments, a subset of relationship attribute types refers to a collection of at least one relationship attribute type aggregated. The subset of relationship attribute types does not refer specifically to a fixed set. For example, when a subset of relationship attribute information changes, the subset of relationship attribute types may also change. When a relationship attribute type changes, the subset of relationship attribute types may also change.

In some embodiments, the relationship attribute type subset refers to a set formed by aggregating at least one relationship attribute type corresponding to any entity in the relationship attribute type subset. The subset of relationship attribute types does not refer specifically to a fixed set. For example, when a subset of relationship attribute types changes, the subset of relationship attribute types may also change. When a relationship attribute type changes, the subset of relationship attribute types may also change.

According to some embodiments, when the terminal acquires the relationship attribute identifier subset and the relationship attribute type subset, the terminal may acquire the relationship attribute information subset corresponding to any entity based on the relationship attribute identifier subset and the relationship attribute type subset. The terminal can obtain the relationship attribute information subset corresponding to at least one entity by traversing the first entity set. Therefore, the terminal can improve the accuracy of the OWL system construction by acquiring the relationship attribute information subset, and further improve the use experience of the user.

In some embodiments, the relationship attribute information subset refers to a set formed by converging relationship attribute information corresponding to any entity. The relationship attribute information subset does not refer to a fixed information. For example, when an entity corresponding to a subset of relationship attribute information changes, the subset of relationship attribute information may also change. When the relationship attribute information changes, the subset of relationship attribute information may also change.

For example, fig. 7 shows a schematic structural diagram of a relationship attribute information subset according to an embodiment of the present application. As shown in fig. 7, "person M" is a legal person of "agency N1"; is a major member of "agency N2".

For example, when entity E, F, G is included in the first set of entities, the terminal can traverse the first set of entities to obtain relationship attribute information between entities E, F, G. Further, the terminal may obtain a subset of relationship attribute information corresponding to entity E, F, G.

It is easy to understand that, when the terminal acquires the first entity set, the terminal may acquire the subset of the object attribute information corresponding to at least one entity in the first entity set. The terminal may also obtain a data attribute information subset corresponding to at least one entity in the first entity set. The terminal can also obtain a relation attribute information subset corresponding to at least one entity.

Step S206: constructing an initial web ontology language (OWL) system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset;

according to some embodiments, fig. 8 shows a presentation diagram of an initial OWL according to an embodiment of the present application. As shown in fig. 8, when the terminal performs the initial OWL presentation, the number of entities owned in the object attribute information subset acquired by the terminal, the specific entities, and the number information corresponding to each entity may be presented. The terminal can also display the specific relation attribute information and the corresponding quantity information in the relation attribute information subset.

It is easy to understand that when the terminal acquires the object attribute information subset, the data attribute information subset, and the relationship attribute information subset, the terminal may construct an initial OWL based on the object attribute information subset, the data attribute information subset, and the relationship attribute information subset.

Step S207: acquiring a bid document in a target document format through a Robot Process Automation (RPA) system, and identifying the bid document by adopting an Optical Character Recognition (OCR) technology to obtain a structured bid document;

according to some embodiments, the document format refers to a particular encoding of the textual information used by the computer to store the textual information. The Document Format includes, but is not limited to, text txt Format, HTML Format, word Format, Portable Document Format (PDF) Format, and the like. The target document format refers to the document format of the acquired bid document selected by the RPA system. The target document format does not feature a fixed format. For example, when the terminal acquires a format modification instruction for a target document format, the target document format may be changed accordingly.

According to some embodiments, when the terminal acquires any bidding document, the terminal can acquire a document format corresponding to the any bidding document; and if the document format is not the target document format, carrying out format conversion on the bidding document through the RPA system to obtain the bidding document in the target document format. Therefore, the adaptability of the RPA system to bidding documents in different document formats can be improved, and the document acquisition accuracy is improved.

In some embodiments, when the terminal performs format conversion on the bid document through the RPA system, the terminal may perform format conversion on the bid document by using a document format conversion tool built in the RPA system. For example, when the terminal acquires a bid document in a word format, the terminal may call a word underlying macro language (VBA) using a python win32 library to convert the bid document in the word format into a bid document in a PDF format.

For example, when the target document format set by the terminal is PDF format, if the terminal acquires the bid document H in word format through the RPA system. The terminal can convert the format of the bid document H into a bid document in PDF format by using a document format conversion tool built in the RPA system, as shown in fig. 9.

According to some embodiments, a structured bidding document refers to a document that consists of logical structures, such as titles, chapters, paragraphs, and the like. The structured bid document does not refer specifically to a fixed document. For example, when the content of a bid document changes, the structured bid document may also change accordingly. When the document format of the bid document changes, the structured bid document may also change accordingly.

In some embodiments, when the terminal identifies the bid document by using the OCR technology, the context information of the bid document, such as paragraph information, may be lost. Therefore, when the terminal identifies the bidding document in the format of the at least one target document by using the OCR technology, the terminal needs to acquire content information and structure information corresponding to the at least one bidding document. Furthermore, the terminal may perform document structure restoration on the bid-for document based on the content information and the structure information, and the restored bid-for document may combine character information by paragraph.

In some embodiments, the content information refers to a set of character information for each character in the bid document. The content information does not refer to a fixed information. For example, when the content of the bid document changes, the content information may also change. When the document format of the bid document is changed, the content information may also be changed.

In some embodiments, the structural information refers to a set of coordinate information corresponding to each character in the bid document. The structure information does not refer to a fixed information. For example, when the content of the bid document changes, the structural information may also change. When the document format of the bid document changes, the structural information may also change.

In some embodiments, when the terminal performs document structure reduction on the bid-bidding document, the terminal can judge paragraph start and end information of the document according to features of the document such as line spacing, line head, line tail, indentation, line length and the like, and insert a line feed character mark between paragraphs to realize paragraph reduction. The terminal can also detect whether a catalogue exists in the bidding document, if so, the content of the catalogue is identified, the chapter position is positioned according to the catalogue, and the chapter structure of the bidding document is restored according to the chapter position. If the catalog does not exist, the chapter position is positioned according to the characteristics of the title and the line spacing of the bidding document, and the chapter structure of the bidding document is restored according to the chapter position.

It is easy to understand that when the terminal constructs the initial OWL, the terminal may obtain the bid document in the target document format through the robot flow automation RPA system, and identify the bid document by using an optical character recognition OCR technology to obtain a structured bid document.

Step S208: acquiring a target object attribute information subset and a target relation attribute information subset corresponding to a web ontology language OWL system from a structured bidding document;

according to some embodiments, the target object attribute information subset refers to a set formed by aggregating entity attribute information required for constructing the target OWL system. The subset of object attribute information does not refer to a fixed set. For example, when a structured bidding document changes, the subset of target object attribute information may also change. When the target OWL system changes, the target object attribute information subset may also change.

In some embodiments, the target relationship attribute information subset refers to a set obtained by aggregating relationship attribute information required for constructing the target OWL system. The target relationship attribute information subset does not refer to a fixed set. For example, when the structured bid document changes, the target relationship attribute information subset may also change. When the target OWL system changes, the target relationship attribute information subset may also change.

It is easy to understand that when the terminal acquires the structured bid document, the terminal may acquire the target object attribute information subset and the target relationship attribute information subset corresponding to OWL in the structured bid document.

Step S209: and constructing a target OWL system based on the target object attribute information subset and the target relation attribute information subset.

According to some embodiments, a target OWL system refers to the OWL to which the bid document corresponds. The target OWL system is not specific to a fixed OWL. For example, when a subset of the target object attribute information changes, the target OWL system may also change. When a target relationship attribute information subset changes, the target OWL system may also change.

It is easy to understand that when the terminal acquires the target object attribute information subset and the target relationship attribute information subset, the terminal may construct the target OWL system based on the target object attribute information subset and the target relationship attribute information subset.

In the embodiment of the application, the building instruction input aiming at the initial network ontology language OWL system is obtained, the entity set selection interface is displayed in response to the building instruction, the selection instruction input aiming at the second entity set in the entity set selection interface is obtained, and the first entity set input aiming at the initial network ontology language OWL system is obtained in response to the selection instruction, so that the terminal can select the required entity to build the OWL, the building accuracy of the OWL system can be improved, and the use experience of a user is further improved. Secondly, an initial network ontology language OWL system is constructed by acquiring an attribute information set corresponding to at least one entity in a first entity set, wherein the attribute information set comprises an object attribute information subset, a data attribute information subset and a relationship attribute information subset, and the OWL system is constructed based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset, so that the accuracy of constructing the OWL system can be improved, and the use experience of a user can be improved. In addition, the robot process automation RPA system acquires the bid document in the target document format, so that the adaptability of the RPA system to bid documents in different document formats can be improved, and the accuracy of acquiring the target object attribute information subset and the target relation attribute information subset corresponding to the OWL is improved. And then, identifying the bidding document by adopting an Optical Character Recognition (OCR) technology to obtain a structured bidding document, so that the document structure of the bidding document can be restored, and the efficiency of acquiring the target object attribute information subset and the target relationship attribute information subset corresponding to the OWL is improved. And finally, acquiring a target object attribute information subset and a target relation attribute information subset corresponding to the web ontology language OWL system from the structured bid-inviting document, and constructing the target OWL system based on the target object attribute information subset and the target relation attribute information subset, so that an OWL applied to the bid-inviting field can be constructed, and the use experience of a user can be improved.

Fig. 10 is a flowchart illustrating a method for constructing an OWL system combining RPA and AI according to an embodiment of the present application, where as shown in fig. 10, the method may include the following steps:

step S301: acquiring a construction instruction input by aiming at an initial web ontology language (OWL) system;

Step S302: displaying an entity set input interface in response to the construction instruction;

it is easy to understand that the entity set input interface refers to an interface for inputting a first entity set, and the entity set input interface does not refer to a fixed input interface. For example, when the display position of the input control in the entity set input interface changes, the entity set input interface can also change correspondingly. When the terminal acquires the construction instruction input for the initial OWL, the terminal can respond to the construction instruction and display the entity set input interface.

Step S303: acquiring a first entity set input in an entity set input interface;

according to some embodiments, fig. 11 shows a presentation diagram of an entity collection input interface according to an embodiment of the present application. As shown in fig. 11, the user may input an entity to be used in a text box below the entity set based on the entity set input interface, and click a "complete" button after the input is completed. Further, the terminal may obtain the first entity set input in the entity set input interface.

It is easy to understand that, when the terminal presents the entity set input interface, the user may input a required first entity set based on the entity set input interface, and then, the terminal may acquire the first entity set input in the entity set input interface.

Step S304: acquiring an entity type set corresponding to each entity in a first entity set;

according to some embodiments, entity type refers to

In some embodiments, a set of entity types refers to a set of at least one entity type aggregated. The set of entity types does not refer to a fixed set. For example, when an entity type changes, the set of entity types may also change. When a first set of entities changes, the set of entity types may also change.

It is easy to understand that, when the terminal acquires the first entity set, the terminal may acquire an entity type set corresponding to each entity in the first entity set.

Step S305: and constructing an initial web ontology language (OWL) system corresponding to the entity type set.

It is easy to understand that when the terminal acquires the entity type set, the terminal may construct an initial network ontology language OWL system corresponding to the entity type set.

In the embodiment of the application, an entity set input interface is displayed in response to a construction instruction by acquiring the construction instruction input by aiming at an initial web ontology language (OWL) system; the first entity set input in the entity set input interface is obtained, so that a required OWL body can be constructed by selecting the input first entity set, and the use experience of a user can be improved. And secondly, acquiring an entity type set corresponding to each entity in the first entity set, and constructing an initial web ontology language (OWL) system corresponding to the entity type set, so that the corresponding OWL can be constructed according to the required entity type set, the construction accuracy of the OWL system can be improved, the application scene of the OWL is enriched, and the use experience of a user can be improved.

The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.

Please refer to fig. 12, which is a schematic structural diagram of an OWL system constructing apparatus combining RPA and AI according to an embodiment of the present application. The OWL system constructing means combining RPA and AI may be implemented as all or a part of the apparatus by software, hardware or a combination of both. The OWL system constructing apparatus 1200 combining RPA and AI includes a set acquiring unit 1201, an information acquiring unit 1202, and a system constructing unit 1203, where:

a set acquiring unit 1201, configured to acquire a first entity set input for an initial web ontology language OWL system;

an information obtaining unit 1202, configured to obtain an attribute information set corresponding to at least one entity in a first entity set, where the attribute information set includes an object attribute information subset, a data attribute information subset, and a relationship attribute information subset;

a system constructing unit 1203, configured to construct an initial web ontology language OWL system based on the object attribute information subset, the data attribute information subset, and the relationship attribute information subset.

Fig. 13 is a schematic structural diagram of an OWL system constructing apparatus combining RPA and AI according to an embodiment of the present application. As shown in fig. 13, the OWL system constructing apparatus 1200 further includes a document acquiring unit 1204, an attribute acquiring unit 1205 and an OWL constructing unit 1206, configured to, after constructing the initial web ontology language OWL system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset:

a document acquiring unit 1204, configured to acquire a bid document in a target document format through a robot process automation RPA system, and identify the bid document by using an optical character recognition OCR technology to obtain a structured bid document;

an attribute obtaining unit 1205, configured to obtain, in the structured bid-inviting and bidding document, a target object attribute information subset and a target relationship attribute information subset corresponding to the initial web ontology language OWL system;

an OWL constructing unit 1206, configured to construct a target OWL system based on the target object attribute information subset and the target relationship attribute information subset.

According to some embodiments, fig. 14 is a schematic structural diagram of an OWL system constructing apparatus combining RPA and AI according to an embodiment of the present application. As shown in fig. 14, the set acquiring unit 1201 includes a construction instruction acquiring subunit 1211, a selection interface presentation subunit 1221, a selection instruction acquiring subunit 1231, and an instruction response subunit 1241, where when the set acquiring unit 1201 is configured to acquire a first entity set input for the initial web ontology language OWL system:

a construction instruction obtaining subunit 1211, configured to obtain a construction instruction input for the initial network ontology language OWL system;

a selection interface presentation subunit 1221, configured to, in response to the construction instruction, present an entity set selection interface;

a selection instruction obtaining subunit 1231, configured to obtain a selection instruction input for a second entity set in the entity set selection interface;

the instruction response subunit 1241 is configured to, in response to the selection instruction, obtain the first entity set input for the initial web ontology language OWL system.

According to some embodiments, fig. 15 is a schematic structural diagram of an OWL system constructing apparatus combining RPA and AI according to an embodiment of the present application. As shown in fig. 15, the set acquiring unit 1201 further includes an instruction acquiring subunit 1251, an interface showing subunit 1261, and an entity set acquiring subunit 1271, where the set acquiring unit 1201 is configured to, when acquiring the first entity set input for the initial web ontology language OWL system:

an instruction obtaining subunit 1251, configured to obtain a construction instruction input for the initial web ontology language OWL system;

an interface display subunit 1261, configured to display an entity set input interface in response to the build instruction;

an entity set acquiring subunit 1271, configured to acquire the first entity set input in the entity set input interface.

According to some embodiments, fig. 16 is a schematic structural diagram of an OWL system constructing apparatus combining RPA and AI according to an embodiment of the present application. As shown in fig. 16, the attribute information subset includes an attribute identification subset and an attribute type subset, the information obtaining unit 1202 includes an identification obtaining subunit 1212, a type obtaining subunit 1222, a subset obtaining subunit 1232, and a set traversal subunit 1242, and the information obtaining unit 1202 is configured to obtain the attribute information subset corresponding to at least one entity in the first entity set;

an identifier obtaining subunit 1212, configured to obtain an attribute identifier subset corresponding to any entity in the first entity set;

a type obtaining subunit 1222, configured to obtain a subset of attribute types corresponding to any entity;

a subset obtaining subunit 1232, configured to obtain, based on the attribute identifier subset and the attribute type subset, an attribute information subset corresponding to any entity;

the set traversing subunit 1242 is configured to traverse the first entity set to obtain an attribute information subset corresponding to at least one entity.

According to some embodiments, fig. 17 is a schematic structural diagram of an OWL system constructing apparatus combining RPA and AI according to an embodiment of the present application. As shown in fig. 17, the OWL system constructing apparatus 1200 further includes:

a type set acquiring unit 1207, configured to acquire an entity type set corresponding to each entity in the first entity set;

a system constructing unit 1203, configured to construct an initial web ontology language OWL system corresponding to the entity type set.

The functions of each module in each apparatus in the embodiment of the present application may refer to corresponding descriptions in the above method, and are not described herein again.

In the embodiment of the application, a first entity set input by an initial web ontology language OWL system is acquired through a set acquisition unit; the information acquisition unit acquires an attribute information set corresponding to at least one entity in the first entity set, wherein the attribute information set comprises an object attribute information subset, a data attribute information subset and a relation attribute information subset; and the system construction unit constructs an initial web ontology language (OWL) system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset. The user can construct the required OWL ontology by selecting the first set of entities entered. By designing the object attribute information, the data attribute information and the relationship attribute information and constructing the OWL based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset, the accuracy of constructing the OWL system can be improved, the application scenes of the OWL are enriched, and the use experience of a user can be improved.

Fig. 18 shows a block diagram of a terminal according to an embodiment of the present application. As shown in fig. 18, the terminal includes: a memory 1810 and a processor 1820, the memory 1810 having stored therein computer programs operable on the processor 1820. The processor 1820, when executing the computer program, implements the method for constructing an OWL system by combining RPA and AI in the above embodiments. The number of the memory 1810 and the processor 1820 may be one or more.

The terminal further comprises:

the communication interface 1830 is used to communicate with an external device for data interactive transmission.

If the memory 1810, the processor 1820, and the communication interface 1830 are implemented independently, the memory 1810, the processor 1820, and the communication interface 1830 may be connected to each other by a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 18, but this does not mean only one bus or one type of bus.

Alternatively, in an implementation, if the memory 1810, the processor 1820 and the communication interface 1830 are integrated on one chip, the memory 1810, the processor 1820 and the communication interface 1830 may communicate with each other through an internal interface.

Embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, the computer program implements the method provided in the embodiments of the present application.

The embodiment of the present application further provides a chip, where the chip includes a processor, and is configured to call and execute the instruction stored in the memory from the memory, so that the communication device in which the chip is installed executes the method provided in the embodiment of the present application.

An embodiment of the present application further provides a chip, including: the system comprises an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected through an internal connection path, the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the method provided by the embodiment of the application.

It should be understood that the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be an advanced reduced instruction set machine (ARM) architecture supported processor.

Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct memory bus RAM (DR RAM).

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the present application are generated in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes other implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the method of the above embodiments may be implemented by hardware that is configured to be instructed to perform the relevant steps by a program, which may be stored in a computer-readable storage medium, and which, when executed, includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module may also be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for constructing an OWL system by combining RPA and AI is characterized by comprising the following steps:

acquiring an attribute information set corresponding to at least one entity in the first entity set, wherein the attribute information set comprises an object attribute information subset, a data attribute information subset and a relation attribute information subset;

and constructing the initial web ontology language OWL system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset.

2. The method according to claim 1, wherein after said constructing an initial web ontology language OWL system based on said object attribute information subset, said data attribute information subset and said relationship attribute information subset, further comprises:

acquiring a target object attribute information subset and a target relation attribute information subset corresponding to the initial web ontology language OWL system from the structured bidding document;

3. The method of claim 1, wherein the obtaining the first set of entities input for the initial web ontology language (OWL) system comprises:

and responding to the selection instruction, and acquiring a first entity set input aiming at the initial web ontology language OWL system.

4. The method of claim 1, wherein the obtaining the first set of entities input for the initial web ontology language (OWL) system comprises:

acquiring a construction instruction input by aiming at an initial network ontology language (OWL) system;

acquiring a first entity set input in the entity set input interface.

5. The method according to claim 1, wherein the attribute information set includes an attribute identifier subset and an attribute type subset, and the obtaining the attribute information set corresponding to at least one entity in the first entity set includes:

acquiring the attribute identifier subset corresponding to any entity in the first entity set;

acquiring the attribute type subset corresponding to any entity;

and traversing the first entity set to acquire an attribute information set corresponding to the at least one entity.

6. The method of claim 1, further comprising:

acquiring an entity type set corresponding to each entity in the first entity set;

7. An OWL system construction device combining RPA and AI, characterized by comprising:

an information obtaining unit, configured to obtain an attribute information set corresponding to at least one entity in the first entity set, where the attribute information set includes an object attribute information subset, a data attribute information subset, and a relationship attribute information subset;

and the system construction unit is used for constructing the initial web ontology language OWL system based on the object attribute information subset, the data attribute information subset and the relationship attribute information subset.

8. The apparatus according to claim 7, wherein said apparatus further comprises a document obtaining unit, an attribute obtaining unit, and an OWL constructing unit, configured to, after said constructing an initial web ontology language OWL system based on said object attribute information subset, data attribute information subset, and said relationship attribute information subset:

the document acquisition unit is used for acquiring the bid document in a target document format through a Robot Process Automation (RPA) system, and identifying the bid document by adopting an Optical Character Recognition (OCR) technology to obtain a structured bid document;

the attribute obtaining unit is configured to obtain a target object attribute information subset and a target relationship attribute information subset corresponding to the web ontology language OWL system in the structured bid-inviting document;

9. The apparatus according to claim 7, wherein the set obtaining unit includes a build instruction obtaining subunit, a selection interface showing subunit, a selection instruction obtaining subunit, and an instruction response subunit, and when the set obtaining unit is configured to obtain the first entity set input for the initial web ontology language OWL system:

the building instruction acquisition subunit is used for acquiring a building instruction input by aiming at the initial network ontology language OWL system;

the selection instruction acquisition subunit is configured to acquire a selection instruction input for a second entity set in the entity set selection interface;

the instruction response subunit is configured to, in response to the selection instruction, obtain a first entity set input for the initial web ontology language OWL system.

10. The apparatus according to claim 7, wherein the set obtaining unit further includes an instruction obtaining subunit, an interface showing subunit, and an entity set obtaining subunit, and when the set obtaining unit is configured to obtain the first entity set input for the initial web ontology language OWL system:

the entity set obtaining subunit is configured to obtain the first entity set input in the entity set input interface.

11. The apparatus according to claim 7, wherein the attribute information set includes an attribute identifier subset and an attribute type subset, the information obtaining unit includes an identifier obtaining subunit, a type obtaining subunit, a subset obtaining subunit, and a set traversal subunit, and the information obtaining unit is configured to obtain the attribute information set corresponding to at least one entity in the first entity set;

the identifier obtaining subunit is configured to obtain the attribute identifier subset corresponding to any entity in the first entity set;

the type obtaining subunit is configured to obtain the attribute type subset corresponding to the any entity;

the subset obtaining subunit is configured to obtain, based on the attribute identifier subset and the attribute type subset, an attribute information subset corresponding to the any entity;

and the set traversing subunit is configured to traverse the first entity set to obtain an attribute information set corresponding to the at least one entity.

12. The apparatus of claim 7, further comprising:

a type set obtaining unit, configured to obtain an entity type set corresponding to each entity in the first entity set;

13. A RPA and AI combined terminal, comprising: a processor and a memory, the memory having stored therein instructions that are loaded and executed by the processor to implement the method of any of claims 1-6.

14. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.