CN113326381A - Semantic and knowledge graph analysis method, platform and equipment based on dynamic ontology - Google Patents
Semantic and knowledge graph analysis method, platform and equipment based on dynamic ontology Download PDFInfo
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Abstract
The application discloses a semantic and knowledge graph analysis method, a platform and equipment based on a dynamic body, wherein the method comprises the following steps: acquiring data to be processed; performing text semantic analysis and ontology mapping processing on the data to be processed, and extracting an ontology structure, wherein the ontology structure comprises an object, an attribute and a relation; and constructing a knowledge graph library based on the relation among the objects, the attributes and the relations so as to analyze and process the data to be processed. The technical scheme integrates multiple technologies such as dynamic ontology knowledge modeling, knowledge map technology and big data technology, integration and processing of multi-source heterogeneous data are achieved, the ontology structure can be automatically extracted through text semantic analysis and a ontology mapping processing method, a knowledge map library is further constructed, fast association mining of massive multi-source heterogeneous data is achieved, comprehensive analysis can be conducted on the multi-source heterogeneous data, and the use value of the data is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a semantic and knowledge graph analysis method, a semantic and knowledge graph analysis platform and a semantic and knowledge graph analysis device based on a dynamic ontology.
Background
With the rapid development of internet technology, artificial intelligence has become a competitive focus in various fields, and various industries step on roads for intelligent upgrading and transformation, so that the demand for intelligent application is increased, and the comprehensive analysis of big data is particularly important in order to meet the need of artificial intelligence on massive multi-source heterogeneous data and data association mining.
At present, tools used for analyzing multi-source heterogeneous data in the prior art are more traditional, the requirement on the data format of the data is higher, Chinese processing support is poor, comprehensive analysis on the data cannot be comprehensively carried out, and the use value of the data is greatly reduced.
Disclosure of Invention
In view of the above defects or shortcomings in the prior art, it is desirable to provide a semantic and knowledge graph analysis method, a platform, a device and a medium based on a dynamic ontology, which can construct a knowledge graph library through text semantic analysis and ontology mapping processing, so that comprehensive analysis can be performed on multi-source heterogeneous data, the use value of the data is improved to a great extent, and the conversion from the data to knowledge is rapidly realized.
In a first aspect, an embodiment of the present application provides a semantic and knowledge graph analysis method based on a dynamic ontology, where the method includes:
acquiring data to be processed;
performing text semantic analysis and ontology mapping processing on the data to be processed, and extracting an ontology structure, wherein the ontology structure comprises an object, an attribute and a relation;
and constructing a knowledge map library based on the relation among the objects, the attributes and the relations.
In one embodiment, the text semantic analysis processing and ontology mapping processing are performed on the data to be processed, and extracting an ontology structure includes:
defining ontology structural rules through a data knowledge processing tool;
registering a database table, wherein the database table is used for extracting knowledge;
and obtaining an ontology structure based on the database table and the text structure rule.
In one embodiment, building a knowledge graph library based on the relationships between the objects, attributes and relationships comprises:
performing data cleaning processing on the data to be processed to obtain processed data;
constructing a core field ontology library according to the corresponding relation between the ontology structure and the knowledge;
and obtaining a knowledge graph library based on the core field ontology library and the preprocessed data.
In one embodiment, the data to be processed is subjected to data cleaning processing to obtain processed data, and the data cleaning processing includes:
detecting the data to be processed according to a data cleaning rule, and determining the category of the data to be processed, wherein the category comprises normal, known abnormity and unknown abnormity;
and obtaining the processed data based on the category of the data to be processed and the data cleaning rule.
In one embodiment, building a core domain ontology library according to the corresponding relationship between the ontology structure and knowledge includes:
carrying out knowledge arrangement on the acquired knowledge, and extracting domain knowledge;
and constructing a core domain ontology base based on the domain knowledge and the mapping rule of the ontology structure.
In one embodiment, after the knowledge map library is constructed, the method further comprises:
managing the knowledge map library by using a knowledge map platform;
and providing the map computing service to the outside through a service interface based on the encapsulation service rule of the knowledge map platform.
In one embodiment, the service interface comprises at least one of: the system comprises an ontology service interface, a data access service interface, a knowledge graph analysis service interface and an operation state service interface.
In a second aspect, the present application provides a dynamic ontology-based semantic and knowledge-graph analysis platform, comprising:
the acquisition module is used for acquiring data to be processed;
the extraction module is used for performing text semantic analysis processing and ontology mapping processing on the data to be processed and extracting an ontology structure, wherein the ontology structure comprises an object, an attribute and a relation;
and the construction module is used for constructing a knowledge map library based on the relation among the objects, the attributes and the relations.
In a third aspect, an embodiment of the present application provides an apparatus, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for semantic and knowledge-graph analysis based on dynamic ontology as described in the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, the computer program being configured to implement the method for semantic and knowledge-graph analysis based on dynamic ontology as described in the first aspect above.
According to the semantic and knowledge graph analysis method, the platform, the equipment and the storage medium based on the dynamic ontology, the ontology structure is extracted by acquiring the data to be processed and performing text semantic analysis and ontology mapping processing on the data to be processed, the ontology structure comprises objects, attributes and relations, and a knowledge graph library is constructed based on the relations among the objects, the attributes and the relations. The technical scheme integrates multiple technologies such as dynamic ontology knowledge modeling, knowledge map technology and big data technology, integration and processing of multi-source heterogeneous data are achieved, the ontology structure can be automatically extracted through text semantic analysis and a ontology mapping processing method, a knowledge map library is further constructed, fast association mining of massive multi-source heterogeneous data is achieved, comprehensive analysis can be conducted on the multi-source heterogeneous data, the use value of the data is improved, conversion from the data to the knowledge is achieved fast, and development of each business field to artificial intelligence is promoted.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a diagram of an implementation environment architecture for a dynamic ontology-based semantic and knowledge-graph analysis method provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram of a semantic and knowledge-graph analysis method based on dynamic ontology according to an embodiment of the present application;
FIG. 3 is a schematic flow chart diagram of a semantic and knowledge-graph analysis method based on dynamic ontology according to an embodiment of the present application;
FIG. 4 is a schematic flow chart diagram illustrating a method for semantic and knowledge-graph analysis based on dynamic ontology according to another embodiment of the present application;
fig. 5 is a schematic flowchart illustrating a method for providing a map service to the outside through a service interface according to another embodiment of the present application;
FIG. 6 is a schematic structural diagram of a semantic and knowledge-graph analysis platform based on dynamic ontology according to an embodiment of the present application;
FIG. 7 is an architectural diagram of a dynamic ontology-based semantic and knowledge-graph analysis platform according to another embodiment of the present application;
fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
As mentioned in the background art, in the internet era of rapid development of artificial intelligence, a large amount of heterogeneous data is generated, in order to meet the requirement of artificial intelligence on data association mining, especially in the face of sudden social public security events, various heterogeneous data needs to be tracked and processed to timely respond to the sudden events, so that comprehensive analysis of large data is very important.
Based on the defects, the application provides a semantic and knowledge graph analysis method based on a dynamic ontology, which can realize integrated processing of multi-source heterogeneous data, and a knowledge graph library is constructed through text semantic analysis and ontology mapping processing, so that comprehensive analysis can be performed on the multi-source heterogeneous data based on the knowledge graph library, the use value of the data is improved to a great extent, and the conversion from the data to knowledge is quickly realized.
The semantic and knowledge graph analysis method based on the dynamic ontology can be applied to different fields of government affairs, public safety, network safety, media and the like, not only provides comprehensive support for analysis scenes such as multi-source heterogeneous data fusion, storage, knowledge processing, graph association mining analysis, geographic space analysis, time sequence analysis, visual display and the like for national safety, public safety, government decision and enterprise operation, but also integrates big data technology, visual technology and knowledge graph related technology, can perform knowledge processing on massive heterogeneous data, and achieves knowledge construction management, knowledge semantic retrieval, intelligent text extraction, intelligent question answering, intelligent recommendation, graph relation analysis, geographic space analysis, knowledge management and the like.
Fig. 1 is an implementation environment architecture diagram of a semantic and knowledge graph analysis method based on a dynamic ontology according to an embodiment of the present application. As shown in fig. 1, the implementation environment architecture includes: a terminal 100 and a server 200.
The terminal 100 may be a terminal device in various AI application scenarios. For example, the terminal 100 may be a smart home device such as a smart television and a smart television set-top box, or the terminal 100 may be a mobile portable terminal such as a smart phone, a tablet computer, and an e-book reader, or the terminal 100 may be a smart wearable device such as smart glasses and a smart watch, which is not limited in this embodiment.
Among them, the terminal 100 may be installed with an AI application based on natural language processing. For example, the AI application may be an intelligent search, intelligent question and answer, or the like application.
The server 200 may be a server, or may be a server cluster composed of several servers, or the server 200 may include one or more virtualization platforms, or the server 200 may be a cloud computing service center.
The server 200 may be a server device that provides a background service for the AI application installed in the terminal 100.
The terminal 100 and the server 200 establish a communication connection therebetween through a wired or wireless network. Optionally, the wireless network or wired network described above uses standard communication techniques and/or protocols. The Network is typically the Internet, but may be any Network including, but not limited to, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a mobile, wireline or wireless Network, a private Network, or any combination of virtual private networks.
For ease of understanding and explanation, the dynamic ontology-based semantic and knowledge-graph analysis methods, platforms, devices, and media provided by the embodiments of the present application are described in detail below with reference to fig. 2 to 8.
Fig. 2 is a flowchart illustrating a semantic and knowledge-graph analysis method based on dynamic ontology according to an embodiment of the present invention, where the method may be executed by a computer device, and the computer device may be the server 200 or the terminal 100 in the system shown in fig. 1, or the computer device may also be a combination of the terminal 100 and the server 200. As shown in fig. 2, the method includes:
and S101, acquiring data to be processed.
Specifically, a knowledge graph analysis platform runs on the computer equipment, and the knowledge graph analysis system provides a data access pipeline and a data knowledge processing tool and supports the butt joint of various database resources, including Oracle, MySQL, PostgreSQL, Kafka and the like.
Optionally, the mode of acquiring the data to be processed by the knowledge graph analysis platform may be directly importing the data to be processed, or importing the data to be processed through a system interface, or importing the data to be processed by accessing a database, or importing the data to be processed through a client, or importing the data through a webpage. The form of importing the to-be-processed data through the client may be importing the data from a relational database, or from a table in an excel format, or importing a specific format or specific data. Importing the to-be-processed data through the web page refers to importing the to-be-processed data into the platform through a web page, for example, the web page content returned by a search engine may be imported into the platform through an internal or external search engine of the terminal device, or the content displayed in the browser may be imported into the platform through an interface.
Optionally, the data to be processed may be structured data or unstructured data, where the structured data refers to data logically expressed and implemented by a two-dimensional table structure, and is stored and managed by a relational database, such as a table in a relational database; the unstructured data has an irregular or incomplete data structure and no predefined data model, and comprises office documents, texts, pictures, HTML, various reports, images, audio/video information and the like in all formats.
S102, performing text semantic analysis and ontology mapping processing on the data to be processed, and extracting an ontology structure, wherein the ontology structure comprises objects, attributes and relations.
Specifically, after the data to be processed is obtained, an ontology structure rule related to the construction of the knowledge graph library can be defined through a data knowledge processing tool, the ontology structure rule comprises entity objects, attributes and relational data tables, data fields and rules, data source access parameter information is set, the parameter information comprises information such as access addresses, ports, user names and passwords, a database table and a file target for knowledge extraction are registered, and the specific fields in the registered database table and the content related to the construction of the knowledge graph are mapped to obtain an ontology structure.
The analysis system can accept data from different data sources, and can design templates and correspond to different data processing components for different types of data sources respectively.
It should be noted that, for different accessed data sources, a data knowledge model configuration management function is provided, and a data knowledge model is visually configured for the accessed data resources, where the data knowledge model is configured corresponding to the data resources. The configuration items comprise model relations and the like between entities in the newly added knowledge model, basic information browsing, available field configuration, data source ID configuration of different data sources and detailed configuration information of the data items, wherein the configuration information comprises configuration information such as fields, attributes, whether a primary key exists or not, whether a label exists or not and the like.
Moreover, the analysis system supports the preview of the defined data model, and a user can view the access process and steps of the data through the preview and process operations such as the operation or mapping of each node on the data.
Optionally, the building management of the modeling data and the modeling processing process of the whole knowledge graph can be performed through a knowledge graph platform, wherein the building management comprises the functions of ontology management, data model management, knowledge construction management, system core components, resource statistics and the like. The knowledge map platform supports accessing different types of data, such as structured data, unstructured data and semi-structured data, and can complete functions of knowledge extraction, attribute value pair extraction, description, automatic labeling, warehousing, entity alignment, previewing and the like in a man-machine interaction mode in the data processing and document browsing processes.
The ontology management supports unified management of all ontology types defined in the knowledge graph platform, and includes editing and maintaining information of object/attribute/relationship names, parent classes, URIs, base classes and the like of the ontology, and may include object type management, attribute type management, relationship type management, type grouping management, object attribute mapping management, relationship mapping management between objects, ontology operation management and the like.
Specifically, the object type management function supports unified management of all objects in the knowledge graph platform, including editing and maintaining information such as object names, URIs, parent classes, base classes, and the like. The attribute type management supports the unified management of all the attributes of all the objects in the knowledge graph platform, and comprises the editing and maintenance of information such as attribute names, URIs, base classes and the like. The relation type management function supports unified management of the relation among all the objects in the knowledge graph platform, and comprises editing and maintaining of information such as names and URIs of all the object relations. The type grouping management supports the unified management of all object types defined in the knowledge graph platform, including the classification of the types, the grouping menu, the editing and the maintenance of the object type contents and the like. The object attribute mapping management function supports the unified management of the incidence relation of all objects and attributes in the knowledge graph platform, and comprises the functions of object list management, representative attribute management, allowed attribute management, disallowed attribute management and the like. The management function of the relationship mapping among the objects supports the unified management of the relationship mapping of all the objects and the objects in the knowledge graph platform, and comprises the functions of object list management, association type management, default associated object, allowed object management, disallowed object management and the like. The ontology operation management function supports unified management of types, URIs, icons and the like of all ontologies in the knowledge graph platform. The ontology file uploading/downloading provides the downloading of the defined ontology file in the knowledge-graph platform and the uploading of a new ontology definition file.
The knowledge construction management is used for supporting the representation of knowledge of concepts, examples, relations, events and the like, supporting service experts to define specific models according to services, and supporting the representation of temporal information and geospatial information. Optionally, the knowledge construction management may include import processing configuration management, import flow configuration management, import template management, knowledge construction cluster management, knowledge construction operation management, and knowledge construction history state browsing.
The import processing configuration management supports configuration management of each data source of the import knowledge graph, and provides process group naming, process group configuration description, data source information, attributes, comments and the like imported by the data sources. The import flow configuration management provides visual data import flow configuration, supports data operation of each node in the data import process by dragging a mouse, and displays information of input data, read-write data, output data, task duration and the like of each data processing node. The import template management supports the process of importing the defined data source into the knowledge graph to form an import template and provides functions of adding, editing, deleting and the like of the import template. When a newly added data source needs to be imported into the knowledge graph, a user can quickly select any template to edit and configure, and the purpose of quickly importing data into the knowledge graph is achieved. The knowledge building cluster management support improves the speed of importing data into the knowledge graph and improves functions of cluster node management, cluster management and the like in a cluster management mode. After the knowledge construction operation is configured through the data model and the importing process, the knowledge construction of the knowledge graph can be started, and various components provided by the background are called to realize the knowledge construction from the data source to the knowledge graph. The knowledge building historical state browsing mainly provides state monitoring and browsing of the whole knowledge graph building process, and displays each historical state condition of the knowledge building process in a visual mode.
S103, constructing a knowledge map library based on the relation among the objects, the attributes and the relations.
Specifically, the knowledge graph library is a semantic network that reveals relationships between entities, and can formally describe real-world objects and their relationships.
Optionally, on the basis of the foregoing embodiment, as shown in fig. 3, the foregoing step S103 may include the following method steps:
and S1031, performing data cleaning processing on the data to be processed to obtain processed data.
S1032, building a core field ontology library according to the corresponding relation between the ontology structure and the knowledge.
And S1033, obtaining a knowledge graph library based on the core field ontology library and the preprocessed data.
Specifically, after the data to be processed is obtained, the data to be processed may be structured data, unstructured data and semi-structured data, and after the structured data, the unstructured data and the semi-structured data are subjected to relationship mining, data analysis, text semantic analysis and the like, entities, attributes, relationships and the like are extracted, the entities include identifications and concepts, and mutual relationships are established according to attribute relationships, space-time relationships, semantic relationships, feature relationships and the like of the entities, so that a knowledge graph library is constructed, wherein the knowledge graph library is a knowledge graph library of heterogeneous multi-dimensional and multi-layered entities and entities with domain characteristics, entities and events, relationships and the like.
Extracting the ontology structure may include entity extraction, relationship extraction, and attribute extraction. The entity extraction, also called named entity recognition, refers to the automatic recognition of named entities from a text dataset. The relationship extraction can be to identify the entity relationship by a method of manually constructing semantic rules and templates, and can be divided into two types of entity relationship extraction based on open entity relationship extraction and entity relationship extraction based on joint reasoning. The attribute extraction is to collect attribute information of a specific entity from different information sources.
Referring to fig. 4, when the to-be-processed data to be accessed is obtained from different data sources, the data sources may be databases, files, mails, attachments or other systems, the to-be-processed data may be structured data, unstructured data and semi-structured data, the unstructured data and the semi-structured data are subjected to data processing, and then a knowledge graph library is constructed, the knowledge graph library is constructed by using a unified dynamic ontology model and a unified data storage architecture, the construction of the knowledge graph library may be divided into two parts, one part is to construct a core domain ontology library, the other part is to perform data cleaning processing on the to-be-processed data, and the processed data is obtained and imported into the knowledge graph library. When the core field ontology library is constructed, the domain ontology construction and the domain knowledge construction are included, the domain and the range of the ontology are analyzed during the domain ontology construction, important terms and concepts in the domain are listed, after an ontology framework is established, classes such as objects, relations and attributes are constructed, and an ontology structure is constructed by using tools; extracting and representing the domain knowledge by integrating the acquired knowledge during domain knowledge construction; and according to the mapping rule of the domain knowledge and the ontology structure, a core domain ontology base is constructed. In order to support the reuse and sharing of the domain knowledge, the formalized canonical representation of the knowledge must be completed by adopting a knowledge modeling technology, and the domain ontology construction and the domain knowledge representation are synchronously completed by adopting a parallel thought according to the corresponding relation between the domain ontology and the knowledge. Meanwhile, knowledge processing can be carried out based on a knowledge graph library, wherein the knowledge processing comprises knowledge object management/analysis, a knowledge editing service interface, text mining, a tag engine, knowledge mining and the like.
It should be noted that when the accessed to-be-processed data is acquired, the to-be-processed data includes structured data, semi-structured data, and unstructured data, and the to-be-processed data may undergo a preparation phase, a detection phase, a positioning phase, a modification phase, and a verification phase. The method comprises the steps of leading structured, semi-structured and unstructured documents into a system in a preparation stage, carrying out butt joint configuration in the system, entering a detection stage, detecting data to be processed by using a data cleaning rule in the detection stage, and determining the types of the data to be processed, wherein the types comprise normal types, known abnormal types, unknown abnormal types and the like, the normal data can be directly led into a knowledge spectrum library, the data system with known abnormal types can be automatically processed and then led into the knowledge spectrum library, the data system with unknown abnormal types cannot be processed, and the data system can enter a correction stage to be manually corrected. After the data to be processed is processed in the detection stage, a detection report can be generated, the error position can be positioned in the detection report, and the data quality of the document can be counted. After the known normal data are automatically corrected and the unknown normal data are manually corrected, the processed data are obtained and are imported into a knowledge map library. And finally, in the verification stage, the data are evaluated for data cleaning and body construction according to the fact that the data enter a knowledge map library and the actual use condition, so that the system resources are continuously improved.
The data deepening application capacity can be comprehensively improved by constructing the knowledge map library, the conversion from data to knowledge to intelligent upgrading is realized, and the development of informatization of various fields to intellectualization can be effectively supported.
In the semantic and knowledge graph analysis method based on the dynamic ontology provided in this embodiment, the data to be processed is obtained, and text semantic analysis and ontology mapping processing are performed on the data to be processed, so as to extract an ontology structure, where the ontology structure includes objects, attributes, and relationships, and a knowledge graph library is constructed based on the relationships among the objects, the attributes, and the relationships. The technical scheme integrates multiple technologies such as dynamic ontology knowledge modeling, knowledge map technology and big data technology, integration and processing of multi-source heterogeneous data are achieved, the ontology structure can be automatically extracted through text semantic analysis and a ontology mapping processing method, a knowledge map library is further constructed, fast association mining of massive multi-source heterogeneous data is achieved, comprehensive analysis can be conducted on the multi-source heterogeneous data, the use value of the data is improved, conversion from the data to the knowledge is achieved fast, and development of each business field to artificial intelligence is promoted.
Further, on the basis of the foregoing embodiment, fig. 5 is a schematic flow chart of a method for providing a map computing service to the outside through a service interface according to an embodiment of the present application, and as shown in fig. 5, the method may include the following steps:
s201, acquiring data to be processed.
S202, performing text semantic analysis and ontology mapping processing on the data to be processed, and extracting an ontology structure, wherein the ontology structure comprises objects, attributes and relations.
S203, constructing a knowledge map library based on the relation among the objects, the attributes and the relations.
And S204, managing the knowledge map library by using a knowledge map platform, and providing map calculation service to the outside through a service interface based on the encapsulation service rule of the knowledge map platform.
Specifically, after the data to be processed is obtained, text semantic analysis and ontology mapping processing can be performed on the data to be processed, an ontology structure is extracted, mutual relations are established according to attribute relations, space-time relations, semantic relations, characteristic relations and the like of entities, a knowledge graph library with domain characteristics, multi-dimensional and multi-layer entities, events, relations and the like is constructed, a knowledge graph platform is used for managing the knowledge graph library and the construction process of the knowledge graph library, and a service interface is provided to upper-layer applications and all business units to provide graph calculation services based on encapsulation service rules of the knowledge graph platform. The map computing service comprises a body service, a map access service, a knowledge retrieval service, a knowledge object editing service, a knowledge map operation service, an operation state service and a map analysis service.
The ontology service implements business encapsulation on the constructed ontology object and provides the ontology object externally in an interface mode, and all retrieved ontology supports are presented in an entity object mode. The ontology service interface supports open use facing other system platforms, an interface in a RESTful format needs to be provided, and when other business systems have new data resources and need to be added to the knowledge graph platform, the ontology service interface can be called to construct a mapping relation between a business data source and the knowledge graph platform, so that business data are imported to the knowledge graph platform.
The data access service interface is comprehensively supported to be opened and used for other systems, an interface in a RESTful format is supported, and when business personnel retrieve or call data in the knowledge graph platform, the data access service interface can be called, so that business application calls the data in the knowledge graph platform.
The knowledge graph analysis service is used for realizing service encapsulation of the constructed knowledge graph and providing services to the outside in an interface mode, and the content is mainly the constructed knowledge graph. The service interface of the knowledge graph analysis service supports open use for all service systems, provides an interface in a RESTful format, and can call the interface of the knowledge graph analysis service when the service systems need to call the analysis capability or the analysis result of the knowledge graph, so that the analysis result of the knowledge graph is provided for each application to use.
The operation state service interface supports open use for the whole project, an interface in a RESTful format needs to be provided, and when a service system needs to call data in a knowledge graph platform or a calling process is abnormal, the operation state service interface can be called, so that the operation state and the data state of the knowledge data management system can be known.
In addition, the knowledge correlation analysis system also provides rich and powerful knowledge roaming exploration and management tools to realize the application function of man-machine conversation. The application functions comprise knowledge semantic retrieval based on knowledge graph, intelligent question answering, intelligent recommendation, graph analysis, knowledge management, geospatial analysis, intelligent algorithm model, knowledge collaborative analysis and the like.
In the embodiment, multiple technologies such as dynamic ontology knowledge modeling, knowledge graph technology, big data technology and visualization technology are fused, data integration can be performed, objects, attributes, incidence relations and the like are automatically extracted by means of semantic analysis of texts through natural language processing, knowledge processing is performed according to entity attribute relations, space-time relations, characteristic relations and the like, a massive multidimensional knowledge graph library with industrial characteristics is constructed, rapid association mining of massive data is achieved, clue discovery and early warning prediction can be achieved, internal knowledge sharing and analysis cooperation of organizations can be achieved, multi-person, remote and cross-department cooperation sharing is supported, and information transfer and knowledge sharing among related organizations are achieved. By adopting a scalable and extensible distributed big data framework, the pain points of high performance, high availability, high concurrency, consistency, agility and the like facing mass data access are effectively solved, and the robust operation of the knowledge graph under the pressure of mass data is ensured. The method adopts the measures of encryption transmission, atomic level control, interface authorization use, perfection of a strict authority system and the like, effectively guarantees knowledge content safety, interface safety, deployment safety and transmission safety, and keeps consistent with the requirements of the national information system safety protection standard.
On the other hand, fig. 6 is a schematic structural diagram of a semantic and knowledge-graph analysis platform based on dynamic ontology according to an embodiment of the present disclosure. The system may be a device in a terminal or a server, as shown in fig. 6, the platform 500 includes:
an obtaining module 510, configured to obtain data to be processed;
an extraction module 520, configured to perform text semantic analysis processing and ontology mapping processing on the to-be-processed data, and extract an ontology structure, where the ontology structure includes an object, an attribute, and a relationship;
a construction module 530 for constructing a knowledge graph library based on the relationships between the objects, attributes and relationships.
Optionally, the extracting module 520 includes:
the setting unit 521 is used for defining an ontology structure rule and setting a data source access parameter through a data knowledge processing tool;
a registering unit 522, configured to register a database table, where the database table is used to extract knowledge;
the first processing unit 523 is configured to obtain an ontology structure based on the database table, the text structure rule, and the data source access parameter.
Optionally, the building block 530 includes:
a cleaning unit 531, configured to perform data cleaning processing on the data to be processed to obtain processed data;
a construction unit 532, configured to construct a core domain ontology library according to the corresponding relationship between the ontology structure and the knowledge;
the second processing unit 533 is configured to obtain a knowledge graph library based on the core domain ontology library and the preprocessed data.
Optionally, the cleaning unit 531 is specifically configured to:
detecting the data to be processed according to a data cleaning rule, and determining the category of the data to be processed, wherein the category comprises normal, known abnormity and unknown abnormity;
and obtaining the processed data based on the category of the data to be processed and the data cleaning rule.
Optionally, the constructing unit 532 is specifically configured to:
carrying out knowledge arrangement on the acquired knowledge, and extracting domain knowledge;
and constructing a core domain ontology base based on the domain knowledge and the mapping rule of the ontology structure.
Optionally, the platform further comprises:
a management module 540, configured to manage the knowledge-graph library by using a knowledge-graph platform;
and an interface service module 550, configured to provide an atlas computing service to the outside through a service interface based on the encapsulation service rule of the knowledge atlas platform.
Optionally, the service interface includes at least one of: the system comprises an ontology service interface, a data access service interface, a knowledge graph analysis service interface and an operation state service interface.
Referring to fig. 7, the semantic and knowledge graph analysis platform based on dynamic ontology may be applied to the fields of government affairs, public security, network security, etc., may process the accessed data through a data access manner and a data knowledge processing tool, and construct and manage the whole process of constructing the knowledge graph library through a knowledge graph platform, and may include ontology management, data model management, knowledge construction management, platform core components, resource statistics, log management, user management, etc., and the system provides related packaging services based on the knowledge graph platform, such as ontology service, data access service, operation state service, graph analysis service, etc., and provides rich and powerful knowledge graph applications, which may include knowledge retrieval, intelligent question and answer, intelligent recommendation, graph analysis, knowledge management, geospatial analysis, etc, Intelligent algorithm models, knowledge collaborative sharing, and the like.
It can be understood that the functions of each functional module of the semantic and knowledge-graph analysis platform based on the dynamic ontology according to this embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the relevant description of the foregoing method embodiment, which is not described herein again.
It should be noted that, the data analysis system based on the dynamic ontology provided in this embodiment adopts a distributed storage architecture, supports structured, semi-structured, and unstructured data storage, and can easily expand laterally, and dynamically expand data types, formats, and the like. The data storage structure is native data format, attribute, relation and the like, and can enable a machine to read and understand data more easily based on data rules and models; the system can be used for searching based on natural semantics, has higher efficiency for complex search, can narrow the range of search results to the meaning most desired by the user, enables the user to quickly find the most desired information, can better understand the information searched by the user, summarizes the content related to the search topic, and helps the user to know the relationship between things. Meanwhile, the retrieval result is displayed in an encyclopedia form, and better service experience is provided for the user. The system realizes inference operation based on the knowledge graph, supports multiple algorithm expansion, automatically infers the system, analyzes the service and greatly saves the study and judgment time. Meanwhile, the platform can rapidly expand the applications of various algorithms, analysis models, relationship mining analysis and the like, rapidly respond to various analysis themes, and has the advantages of small secondary development amount, small investment and quick response.
To sum up, in the semantic and knowledge graph analysis platform based on a dynamic ontology provided in the embodiment of the present application, the obtaining module extracts the ontology structure by obtaining the data to be processed and performing text semantic analysis and ontology mapping processing on the data to be processed by using the extracting module, where the ontology structure includes an object, an attribute and a relationship, and the establishing module establishes the knowledge graph library based on the relationship among the object, the attribute and the relationship. The technical scheme integrates multiple technologies such as dynamic ontology knowledge modeling, knowledge map technology and big data technology, integration and processing of multi-source heterogeneous data are achieved, the ontology structure can be automatically extracted through text semantic analysis and a ontology mapping processing method, a knowledge map library is further constructed, fast association mining of massive multi-source heterogeneous data is achieved, comprehensive analysis can be conducted on the multi-source heterogeneous data, the use value of the data is improved, conversion from the data to the knowledge is achieved fast, and development of each business field to artificial intelligence is promoted.
In another aspect, an apparatus provided by the embodiments of the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method for semantic and knowledge-graph analysis based on dynamic ontology as described above is implemented.
Referring to fig. 8, fig. 8 is a schematic structural diagram of a computer system of a terminal device according to an embodiment of the present application.
As shown in fig. 8, the computer system 300 includes a Central Processing Unit (CPU)301 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage section 303 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the system 300 are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input portion 306 including a keyboard, a mouse, and the like; an output section 307 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. A drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage section 308 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 303, and/or installed from the removable medium 311. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software or hardware. The described units or modules may also be provided in a processor, and may be described as: a processor, comprising: the device comprises an acquisition module, an extraction module and a construction module. The names of these units or modules do not in some cases constitute a limitation on the units or modules themselves, and for example, the acquisition module may also be described as "for acquiring data to be processed".
As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may be separate and not incorporated into the electronic device. The computer readable storage medium stores one or more programs which, when executed by one or more processors, perform the dynamic ontology-based semantic and knowledge-graph analysis methods described herein:
acquiring data to be processed;
performing text semantic analysis and ontology mapping processing on the data to be processed, and extracting an ontology structure, wherein the ontology structure comprises an object, an attribute and a relation;
and constructing a knowledge graph library based on the relation among the objects, the attributes and the relations, wherein the knowledge graph library is used for analyzing and processing the data to be processed.
To sum up, the semantic and knowledge graph analysis method, platform, device, and storage medium based on dynamic ontology provided in this embodiment of the present application extract an ontology structure by obtaining data to be processed and performing text semantic analysis and ontology mapping on the data to be processed, where the ontology structure includes an object, an attribute, and a relationship, and constructs a knowledge graph library based on a relationship between the object, the attribute, and the relationship. The technical scheme integrates multiple technologies such as dynamic ontology knowledge modeling, knowledge map technology and big data technology, integration and processing of multi-source heterogeneous data are achieved, the ontology structure can be automatically extracted through text semantic analysis and a ontology mapping processing method, a knowledge map library is further constructed, fast association mining of massive multi-source heterogeneous data is achieved, comprehensive analysis can be conducted on the multi-source heterogeneous data, the use value of the data is improved, conversion from the data to the knowledge is achieved fast, and development of each business field to artificial intelligence is promoted.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A semantic and knowledge graph analysis method based on dynamic ontology is characterized by comprising the following steps:
acquiring data to be processed;
performing text semantic analysis and ontology mapping processing on the data to be processed, and extracting an ontology structure, wherein the ontology structure comprises an object, an attribute and a relation;
and constructing a knowledge graph library based on the relation among the objects, the attributes and the relations, wherein the knowledge graph library is used for analyzing and processing the data to be processed.
2. The semantic and knowledge-graph analysis method based on dynamic ontology according to claim 1, wherein the text semantic analysis processing and ontology mapping processing are performed on the data to be processed, and the extraction of the ontology structure comprises:
defining an ontology structure rule and setting a data source access parameter through a data knowledge processing tool;
registering a database table, wherein the database table is used for extracting knowledge;
and obtaining an ontology structure based on the database table, the text structure rule and the data source access parameter.
3. The dynamic ontology-based semantic and knowledge-graph analysis method of claim 1, wherein building a knowledge-graph library based on the relationships between the objects, attributes and relationships comprises:
performing data cleaning processing on the data to be processed to obtain processed data;
constructing a core field ontology library according to the corresponding relation between the ontology structure and the knowledge;
and obtaining a knowledge graph library based on the core field ontology library and the preprocessed data.
4. The semantic and knowledge graph analysis method based on dynamic ontology according to claim 3, wherein the step of performing data cleaning processing on the data to be processed to obtain processed data comprises:
detecting the data to be processed according to a data cleaning rule, and determining the category of the data to be processed;
and obtaining the processed data based on the category of the data to be processed and the data cleaning rule.
5. The semantic and knowledge graph analysis method based on dynamic ontology according to claim 3, wherein constructing a core domain ontology library according to the corresponding relation between the ontology structure and knowledge comprises:
carrying out knowledge arrangement on the acquired knowledge, and extracting domain knowledge;
and constructing a core domain ontology base based on the domain knowledge and the mapping rule of the ontology structure.
6. The dynamic ontology-based semantic and knowledge-graph analysis method of claim 1, wherein after building the knowledge-graph library, the method further comprises:
managing the knowledge map library by using a knowledge map platform;
and providing the map computing service to the outside through a service interface based on the encapsulation service rule of the knowledge map platform.
7. The dynamic ontology-based semantic and knowledge-graph analysis method of claim 6, wherein the service interface comprises at least one of: the system comprises an ontology service interface, a data access service interface, a knowledge graph analysis service interface and an operation state service interface.
8. A dynamic ontology-based semantic and knowledge-graph analysis platform, the platform comprising:
the acquisition module is used for acquiring data to be processed;
the extraction module is used for performing text semantic analysis processing and ontology mapping processing on the data to be processed and extracting an ontology structure, wherein the ontology structure comprises an object, an attribute and a relation;
and the construction module is used for constructing a knowledge graph library based on the relation among the objects, the attributes and the relations, and the knowledge graph library is used for analyzing and processing the data to be processed.
9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor being configured to implement the dynamic ontology-based semantic and knowledge-graph analysis method according to any one of claims 1-7 when executing the program.
10. A computer readable storage medium having stored thereon a computer program for implementing the dynamic ontology-based semantic and knowledge-graph analysis method according to any one of claims 1-7.
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