CN112954025B

CN112954025B - Information pushing method, device, equipment and medium based on hierarchical knowledge graph

Info

Publication number: CN112954025B
Application number: CN202110127423.XA
Authority: CN
Inventors: 李明琦
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-01-29
Filing date: 2021-01-29
Publication date: 2023-07-18
Anticipated expiration: 2041-01-29
Also published as: CN112954025A

Abstract

The disclosure provides a method, a device, equipment, a medium and a computer program product for pushing information, and relates to the technical field of artificial intelligence, in particular to the technical fields of knowledge graph, intelligent recommendation and natural language processing. The implementation scheme is as follows: acquiring a retrieval request from user equipment; extracting the attention point of the user from the retrieval information in the retrieval request; determining target knowledge corresponding to the user focus point by the layered knowledge graph based on the user focus point and a pre-constructed layered knowledge graph, wherein the layered knowledge graph comprises a technical field knowledge graph and a non-technical field knowledge graph; and pushing the target knowledge to the user equipment as target content.

Description

Information pushing method, device, equipment and medium based on hierarchical knowledge graph

Technical Field

The present disclosure relates to the field of artificial intelligence, and in particular, to the technical fields of knowledge graph, intelligent recommendation, and natural language processing. In particular, the present disclosure provides a hierarchical knowledge graph based information pushing method, apparatus, electronic device, computer readable storage medium and computer program product.

Background

Artificial intelligence is the discipline of studying the process of making a computer mimic certain mental processes and intelligent behaviors (e.g., learning, reasoning, thinking, planning, etc.) of a person, both hardware-level and software-level techniques. The artificial intelligence hardware technology generally comprises technologies such as a sensor, a special artificial intelligence chip, cloud computing, distributed storage, big data processing and the like, and the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge graph technology and the like.

In large institutions or companies, due to the huge body volume, business is close, employee flows are large, and cross-department communication cooperation is large. Meanwhile, staff need to face a large number of office scenes such as transaction and flow processing, communication, collaboration, knowledge information acquisition and the like. This results in a great deal of effort required by staff to find solutions and to find persons in daily work, resulting in inefficiency of work. Along with the development and popularization of knowledge graph and natural language processing technology, intelligent office based on enterprise knowledge management is increasingly emphasized, and the intelligent office becomes a key path for improving enterprise office efficiency.

Disclosure of Invention

The present disclosure provides a hierarchical knowledge graph based information pushing method, apparatus, electronic device, computer readable storage medium and computer program product.

According to an aspect of the present disclosure, there is provided a computer-implemented hierarchical knowledge-graph-based information pushing method, including: acquiring a search request from user equipment, wherein the search request comprises search information of a user aiming at target content, and performing word segmentation analysis on the search information; extracting the attention points of the user from the retrieval information by using the attention point model; determining target knowledge corresponding to the attention point of the user in a layered knowledge graph according to the attention point of the user and a pre-constructed layered knowledge graph, wherein the layered knowledge graph comprises a technical field knowledge graph and a non-technical field knowledge graph, the technical field knowledge graph comprises a plurality of subdivision technical field knowledge graphs, and the non-technical field knowledge graph comprises a plurality of subdivision non-technical field knowledge graphs; and pushing the target knowledge serving as the target content to the user equipment.

According to yet another aspect of the present disclosure, there is provided a computer apparatus comprising: a memory, a processor and a computer program stored on the memory, wherein the processor is configured to execute the computer program to implement the steps of the method as described in the present disclosure.

According to yet another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a method as described in the present disclosure.

According to yet another aspect of the present disclosure, there is provided a computer program product comprising a computer program, wherein the computer program realizes the steps of the method as described in the present disclosure when being executed by a processor.

In accordance with one or more embodiments of the present disclosure, employees may be helped to locate key questions in the system quickly and efficiently, and a way to solve the questions is quickly found.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, in accordance with an embodiment of the present disclosure;

FIG. 2 illustrates a flowchart of a hierarchical knowledge-graph based information pushing method, according to an embodiment of the present disclosure;

FIG. 3 illustrates a flow chart of constructing a hierarchical knowledge-graph, in accordance with an embodiment of the present disclosure;

FIG. 4 illustrates a flow diagram for building a member representation tag according to an embodiment of the present disclosure;

FIG. 5 illustrates a flow diagram of a segment membership portrait tag according to an embodiment of the present disclosure;

FIG. 6 illustrates a block diagram of a hierarchical knowledge-graph based information pushing device, in accordance with an embodiment of the present disclosure;

fig. 7 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.

The terminology used in the description of the various illustrated examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.

In order to solve the problems in the prior art, the method and the device analyze search information submitted by a user based on semantic analysis in a natural language processing technology, extract attention points of the user, rapidly and accurately position target knowledge based on a hierarchical knowledge graph technology, and order and push the target knowledge, so that the following technical scheme based on hierarchical knowledge graph pushing information is provided.

As used herein, the term "clustering algorithm" refers to the unsupervised classification of entities using the similarity between computing entities; the term "TextCNN" refers to an algorithm that classifies text using convolutional neural networks.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented, in accordance with an embodiment of the present disclosure. Referring to fig. 1, the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. Client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the hierarchical knowledge-graph based push information method. It will be appreciated that this is not limiting, and in some embodiments, the client devices 101, 102, 103, 104, 105, and 106 may have sufficient storage and computing resources such that they are also capable of executing one or more services or software applications that push information methods based on hierarchical knowledge maps.

In some embodiments, server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In some embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of client devices 101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof that are executable by one or more processors. A user operating client devices 101, 102, 103, 104, 105, and/or 106 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be appreciated that a variety of different system configurations are possible, which may differ from system 100. Accordingly, FIG. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

A user may submit a retrieval request containing retrieval information using client devices 101, 102, 103, 104, 105, and/or 106. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that the present disclosure may support any number of client devices.

Client devices 101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, apple iOS, UNIX-like operating systems, linux, or Linux-like operating systems (e.g., google Chrome OS); or include various mobile operating systems such as Microsoft Windows Mobile OS, iOS, windows Phone, android. Portable handheld devices may include cellular telephones, smart phones, tablet computers, personal Digital Assistants (PDAs), and the like. Wearable devices may include head mounted displays and other devices. The gaming system may include various handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a number of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. For example only, the one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture that involves virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. Server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

In some implementations, the server 120 may be a server of a distributed system or a server that incorporates a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The cloud server is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the traditional physical host and virtual private server (VPS, virtual Private Server) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of databases 130 may be used to store information such as audio files and video files. The data store 130 may reside in a variety of locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 130 may be of different types. In some embodiments, the data store used by server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve the databases and data from the databases in response to the commands.

In some embodiments, one or more of databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key value stores, object stores, or conventional stores supported by the file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

According to an exemplary embodiment of the present disclosure, the present disclosure provides a computer-implemented hierarchical knowledge-graph based information pushing method, including: acquiring a retrieval request from user equipment, wherein the retrieval request comprises retrieval information of a user aiming at target content; extracting the attention points of the user from the retrieval information; determining target knowledge corresponding to the attention point of the user in a layered knowledge graph according to the attention point of the user and a pre-constructed layered knowledge graph, wherein the layered knowledge graph comprises a technical field knowledge graph and a non-technical field knowledge graph, the technical field knowledge graph comprises a plurality of subdivision technical field knowledge graphs, and the non-technical field knowledge graph comprises a plurality of subdivision non-technical field knowledge graphs; and pushing the target knowledge as the target content to the user equipment.

Fig. 2 illustrates a flowchart of a hierarchical knowledge-graph based information pushing method 200, according to an embodiment of the present disclosure.

In step S201, the system acquires a retrieval request from a user.

According to some embodiments, a user retrieval request from a client device (e.g., client device 101, 102, 103, 104, 105, or 106 in fig. 1) is received in real-time and user retrieval information is stored, for example, in database 130 in fig. 1.

According to some embodiments, obtaining a retrieval request from a user includes: the system receives a search request from the client device and acquires search information input by a user.

In step S203, the focus of the user is extracted based on the search request including the search information.

According to some embodiments, the user's points of interest are extracted through a point of interest model by word segmentation analysis of the user input.

According to some embodiments, for example, by determining that the user inputs corresponding text information, word segmentation is performed on the text information, part of speech tagging is performed on each word segment, each word segment is analyzed, part of speech of each word segment is analyzed, and word segment analysis is performed.

According to some embodiments, the points of interest of the search information of the user are extracted based on the word segmentation of the user through the trained point of interest model.

In step S205, corresponding target knowledge is determined based on the user attention point and a pre-constructed hierarchical knowledge graph.

According to some embodiments, the pre-constructed hierarchical knowledge-graph includes a technical domain knowledge-graph and a non-technical domain knowledge-graph; the technical field knowledge graph comprises knowledge graphs of a plurality of subdivision technical fields; the knowledge graph of the non-technical field simultaneously comprises a plurality of knowledge graphs for subdividing the non-technical field

According to some embodiments, the technical field knowledge graph is composed of a plurality of sub-divided technical field knowledge graphs together. For example, medical science and technology subdivision knowledge patterns, financial science and technology subdivision knowledge patterns, medical collar subdivision knowledge patterns, big data subdivision knowledge patterns, and the like.

According to some embodiments, the non-technical domain knowledge graph is composed of a plurality of sub-divided non-technical domain knowledge graphs together. For example, party building domain subdivision knowledge patterns, administrative domain subdivision knowledge patterns, property domain subdivision knowledge patterns, customer service domain subdivision knowledge patterns, and the like.

According to some embodiments, the associated entity and knowledge information associated with the entity, determined based on the pre-constructed hierarchical knowledge-graph, are treated as target knowledge.

In step S207, pushing is performed to the user equipment based on the corresponding target knowledge.

According to some embodiments, the system responds to the retrieval request of the user, sorts the target knowledge associated with the retrieval request according to a certain rule, pushes the target knowledge to the user equipment, and solves the problem of the user.

In the method for pushing information based on the hierarchical knowledge graph, provided by the embodiment, the user image of the member is built based on the attribute of the member and the associated knowledge document of the member, so that the search of a response user in the future is realized, and the pushed information not only can contain the associated document knowledge, but also can contain the information of the related member; the hierarchical knowledge graph constructed in the embodiment can accurately position target knowledge in the knowledge graph and quickly respond to the retrieval of a user. In a large-scale mechanism, the quick positioning of the problem is realized, the target information is quickly and accurately pushed in response to the retrieval of a user, and the working efficiency of the mechanism is improved.

According to some embodiments, a hierarchical knowledge-graph provided by exemplary embodiments in the present disclosure is constructed by: acquiring historical data of a plurality of members in a group; building a member portrayal tag for each member based on the historical data of the plurality of members; performing hierarchical classification on member portrayal labels of the plurality of members to obtain a technical class label and a non-technical class label, wherein the technical class label comprises a plurality of subdivision technical labels associated with a plurality of subdivision technical fields, and the non-technical class label comprises a plurality of subdivision non-technical labels associated with a plurality of subdivision non-technical fields; respectively constructing the multiple subdivision technical field knowledge maps and the multiple subdivision non-technical field knowledge maps based on the multiple subdivision technical labels and the multiple subdivision non-technical labels; and respectively forming the technical field knowledge graph and the non-technical field knowledge graph by the plurality of subdivision technical field knowledge graphs and the plurality of subdivision non-technical field knowledge graphs so as to obtain the layered knowledge graph.

Fig. 3 illustrates a flowchart of a hierarchical knowledge-graph based information pushing method 300, according to an embodiment of the present disclosure.

In step S301, history data of a plurality of members within a community is acquired.

According to some embodiments, the member's history data includes the community's own history data and the third party's external history data.

The self-contained historical data includes attribute data describing the attributes of each member within the community and at least one knowledge document maintained by that member. Such as departments, job levels, work groups, and historical behavioral data of employees and knowledge document data associated with the employees in the enterprise's internal software.

External history data from the third party is obtained, the external history data including usage log data of the third party software for each member. For example, the external history data of the third party can crawl the history information of the third party software of the staff by a crawler technology means to obtain the use log data of the third party software.

In step S303, an internal member representation is constructed based on the member history data obtained in step S301.

According to some embodiments, the knowledge documents associated with the internal members are processed and analyzed by using a natural language processing model, and corresponding category labels are added to the corresponding documents; and constructing portrait tags of the internal members based on the category tags and the attribute data of each of the members.

In step S305, member portrait labels of the plurality of members are hierarchically classified to obtain a technical class label and a non-technical class label, where the technical class label includes a plurality of subdivision technical labels associated with a plurality of subdivision technical fields, and the non-technical class label includes a plurality of subdivision non-technical labels associated with a plurality of subdivision non-technical fields.

According to some embodiments, the intra-member portrait tags obtained in step S303 are classified into technical class tags and non-technical class tags by a clustering algorithm.

According to some embodiments, the technical class labels and the non-technical class labels are multi-classified by a deep learning algorithm to obtain a plurality of sub-divided technical class labels and a plurality of sub-divided non-technical class labels. For example, subdivision technology class tags may be artificial intelligence tags, software development tags, signal codec tags, and tags containing other specialized technologies; the subdivision non-technical class labels may be personnel labels, supply chain labels, administrative labels, and labels of non-professional technical class that contain functions.

In step S307, the plurality of subdivision technical field knowledge maps and the plurality of subdivision non-technical field knowledge maps are respectively constructed based on the plurality of subdivision technical class labels and the plurality of subdivision non-technical class labels.

According to some embodiments, based on the plurality of subdivision technical class labels and the plurality of subdivision non-technical class labels, an association relationship between the multimodal data is determined through analysis of the multimodal data model on text data, video, picture voice, and the like.

According to some embodiments, the association relationship between the plurality of modal data may also be extracted by a deep learning algorithm.

According to some embodiments, a knowledge graph is constructed for each of the subdivision technical class labels and the subdivision non-technical class labels, respectively. For example, constructing a label knowledge graph in the technical field of subdivision for technical labels such as artificial intelligent labels, software development labels and the like; and constructing a knowledge graph of the subdivision non-technical field for non-technical labels such as personnel labels, supply chain labels and the like.

In step S309, the multiple sub-division technical domain knowledge patterns and the multiple sub-division non-technical domain knowledge patterns are respectively formed into the technical domain knowledge patterns and the non-technical domain knowledge patterns, so as to obtain the hierarchical knowledge patterns.

According to some embodiments, a knowledge-graph of the technical domain and a knowledge-graph of the non-technical domain are respectively constructed based on the knowledge-graph of the sub-division technical domain and the knowledge-graph of the sub-division non-technical domain constructed in S307.

According to some embodiments, the technical field knowledge graph and the non-technical field knowledge graph are combined into one knowledge graph, the layered knowledge graph is obtained, and the construction of the layered knowledge graph is completed. For example, a knowledge graph comprising technical fields such as artificial intelligence fields, software development fields and the like and a knowledge graph comprising non-technical fields such as personnel fields, administrative fields and the like are jointly constructed into a field knowledge graph, so that the knowledge graph is refined and constructed, and the technical fields and the non-technical fields are associated by the knowledge graph to quickly and accurately position target knowledge.

According to some embodiments, constructing a member portrayal tag for each member based on historical data of the plurality of members includes: analyzing the knowledge documents of the plurality of members using a natural language processing model; adding corresponding category labels to the knowledge documents of the plurality of members based on the analysis results of the natural language processing model; and constructing respective member portrayal labels for the plurality of members based on the category labels and the attribute data of the plurality of members.

Fig. 4 illustrates a flowchart of a hierarchical knowledge-graph based information pushing method 400, according to an embodiment of the present disclosure.

In step S401, the plurality of member documents are subjected to analysis processing based on the natural language processing model.

According to some embodiments, the natural language processing model includes, but is not limited to, a point of interest model, a keyword extraction model, and a chapter topic model. By evaluating the result of the document analysis, the most appropriate model is selected for use.

In step S403, the analysis result is added with a corresponding category label to the knowledge document of the internal member.

According to some embodiments, the class labels of the knowledge documents of the obtained internal members may be class labels of natural language processing, big data, java development, personnel archive management, supply chain purchasing, etc.

In step S405, portrait tags of the plurality of members are constructed based on the category tags and attribute data of each of the plurality of members.

According to some embodiments, member portraits of the internal members are constructed jointly based on the class labels of the knowledge documents of the internal members obtained, in combination with the attribute data of the internal members. For example, based on basic information, age, departments, posts and other basic data of the staff and historical behavior data of the staff, the tags of the staff associated knowledge documents are obtained in step S403, so as to construct portrait tags of the staff together.

According to the embodiment, compared with the staff portrait constructed based on the self attribute of the staff and the label of the knowledge document associated with the staff, the user portrait constructed by the embodiment more accurately reflects the information of the user.

According to some embodiments, the hierarchically classifying the member portrayal labels of the plurality of members includes: dividing member portrait labels of the members into technical class labels and non-technical class labels by using a clustering algorithm; and performing multi-classification on the technical class labels and the non-technical class labels by using a deep learning algorithm to obtain the plurality of subdivision technical labels and the plurality of subdivision non-technical labels.

Fig. 5 illustrates a flowchart of a hierarchical knowledge-graph based information pushing method 500, according to an embodiment of the present disclosure.

In step S501, the image labels of the inner members are classified by using a clustering algorithm.

According to some embodiments, k portrait labels are randomly selected, for example, using a basic clustering algorithm, each portrait label initially representing the center of a cluster; assigning each of the remaining image tags to the nearest cluster based on its distance from the center of each cluster; re-calculating the average value of each cluster, and updating the average value into a new cluster center; the first two steps are repeated until the criterion function converges. And classifying the technical labels and the non-technical labels through a clustering algorithm.

In step S503, the technical class labels and the non-technical class labels classified in S501 are multi-classified based on the deep learning algorithm model.

According to some embodiments, for example, a TextCNN deep learning model is adopted to perform multi-classification on technical class labels, and the technical class labels are subdivided into specific industry fields, such as the technical industry fields of software development fields, machine learning fields and the like; the non-technical labels are classified into the non-technical industry fields such as personnel field, administrative field and the like.

According to the embodiment, the labels are respectively classified twice through a clustering algorithm and a deep learning algorithm, so that a better classification effect is obtained. The knowledge graph constructed based on the finely classified labels can be used for responding to the retrieval of the user, so that the corresponding field, the corresponding entity and the corresponding associated target knowledge can be positioned in the knowledge graph more quickly.

According to some embodiments, the extracting the focus of the user from the retrieved information includes: determining a text string from the retrieved information; word segmentation is carried out on the character strings to obtain one or more words; extracting points of interest of the user from the one or more words using a point of interest model.

According to some embodiments, for example, for a user's search request, the search information contains "how me should go to transact a personal profile", the system first makes word breaks for the text string, then makes the focus "transact" with the focus model, and "personal profile" makes the focus extraction.

According to some embodiments, the determining, according to the focus point of the user and a pre-constructed hierarchical knowledge-graph, target knowledge corresponding to the focus point of the user in the hierarchical knowledge-graph includes: determining associated entities in the hierarchical knowledge graph according to the attention points of the user; and determining target knowledge corresponding to the attention point of the user according to the type label of the knowledge related to the associated entity.

According to some embodiments, the target knowledge includes at least one of: a knowledge document related to a point of interest of the user; and attributes of members associated with points of interest of the user.

According to some embodiments, for example, a "front-end Java code error for X project" is retrieved according to a software test post employee; the system extracts the concerns 'X project' and 'Java' of the staff in the software test post based on the retrieval information; based on the attention points of the user, the system locates the technical domain knowledge graph in the pre-constructed knowledge graph and further locates the subdivision technical class knowledge graph in the software development domain; a knowledge graph of a subdivision technical class in the field of software development is used for determining a knowledge document of at least one of X-item java code writing and staff associated with the knowledge document.

According to the embodiment, based on the constructed hierarchical knowledge graph, the system responds to the search of the user, and aims at some complicated problems of the search, so that the system can not only push the document associated with the search problem, but also locate staff associated with the problem, thereby improving the problem solving efficiency.

According to some embodiments, the pushing the target knowledge to the user equipment as the target content comprises: sorting the target knowledge according to a sorting rule; and pushing the ordered target knowledge to the user equipment.

The ordering rules include, but are not limited to, the following methods:

according to some embodiments, the ranking is from high to low based on the relevance between the target knowledge and the user's points of interest. For example, a scoring model is constructed, the relevance of the user attention point and the target knowledge is evaluated and scored, and the ranking from high to low is performed according to the score.

According to some embodiments, the content quality based on the target knowledge is ranked from high to low. For example, a quality assessment model is established, the determined target knowledge is extracted, the quality assessment model performs quality assessment on the target knowledge, and the ranking is performed from high to low based on content quality.

According to some embodiments, the ranking is performed based on an order of selection by the user, allowing the user to select a ranking rule. For example, the user may select the time of the target knowledge generation as the ranking rule, giving priority to showing the content of the most recent time.

Fig. 6 shows a block diagram of a hierarchical knowledge-graph based information pushing device 600, according to an embodiment of the disclosure.

According to some embodiments, the hierarchical knowledge-graph based information pushing apparatus 600 comprises a retrieval acquisition module 601, a point of interest extraction module 602, an information determination module 603, and an information pushing module 604, wherein the acquisition module 601 is configured to: acquiring a search request from user equipment, wherein the search request comprises search information of a user; the point of interest extraction module 602 is configured to: extracting the attention points of the user by using the attention point model based on the retrieval information of the user; the information determination module 603 is configured to: determining corresponding target knowledge based on user attention points and a pre-constructed layered knowledge graph; and the information push module 604 is configured to: based on the determined target knowledge, the system sorts according to a certain rule and pushes the user equipment.

It should be appreciated that the various modules shown in fig. 6, as well as other potential modules of apparatus 600, may correspond to the various steps in method 200 described with reference to fig. 2. Thus, the operations, features and advantages described above with respect to method 200 are equally applicable to apparatus 600 and the modules comprising it. For brevity, certain operations, features and advantages are not described in detail herein.

According to embodiments of the present disclosure, there is also provided an electronic device, a readable storage medium and a computer program product.

Referring to fig. 7, a block diagram of an electronic device 700 that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in device 700 are connected to I/O interface 705, including: an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. The input unit 706 may be any type of device capable of inputting information to the device 700, the input unit 706 may receive input numeric or character information and generate key signal inputs related to user settings and/or function control of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and/or a remote control. The output unit 707 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 708 may include, but is not limited to, magnetic disks, optical disks. The communication unit 709 allows the device 700 to exchange information/data with other devices through computer networks, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 1302.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the method 200 and variations thereof. For example, in some embodiments, the method 200 and variations thereof may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. When the computer program is loaded into RAM 703 and executed by computing unit 701, one or more steps of method 200 and variants thereof described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method 200 and variants thereof by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely exemplary embodiments or examples, and that the scope of the present invention is not limited by these embodiments or examples but only by the claims following the grant and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the disclosure.

Claims

1. A method for pushing information, comprising:

acquiring a retrieval request from user equipment, wherein the retrieval request comprises retrieval information of a user aiming at target content;

extracting the attention points of the user from the retrieval information;

determining target knowledge corresponding to the attention point of the user in a layered knowledge graph according to the attention point of the user and a pre-constructed layered knowledge graph, wherein the layered knowledge graph comprises a technical domain knowledge graph and a non-technical domain knowledge graph, the technical domain knowledge graph comprises a plurality of subdivision technical domain knowledge graphs, the non-technical domain knowledge graph comprises a plurality of subdivision non-technical domain knowledge graphs, the layered knowledge graph is constructed based on member portraits which are constructed based on member self attributes and knowledge documents associated with the members, and the target knowledge comprises at least one of the following: a knowledge document related to the user's point of interest, and attributes of members related to the user's point of interest; and

Pushing the target knowledge as the target content to the user device,

the hierarchical knowledge graph is constructed through the following steps:

obtaining historical data of a plurality of members within a community, wherein the historical data comprises attribute data describing attributes of each member within the community and at least one knowledge document maintained by the member;

building a member portraits tag for each member based on the historical data of the plurality of members, wherein the member portraits tag is built based on the category tag of the knowledge document of the corresponding member and the attribute data of the member;

performing hierarchical classification on member portrayal labels of the plurality of members to obtain a technical class label and a non-technical class label, wherein the technical class label comprises a plurality of subdivision technical labels associated with a plurality of subdivision technical fields, and the non-technical class label comprises a plurality of subdivision non-technical labels associated with a plurality of subdivision non-technical fields;

respectively constructing the multiple subdivision technical field knowledge maps and the multiple subdivision non-technical field knowledge maps based on the multiple subdivision technical class labels and the multiple subdivision non-technical class labels; and

And respectively forming the technical field knowledge graph and the non-technical field knowledge graph by the plurality of subdivision technical field knowledge graphs and the plurality of subdivision non-technical field knowledge graphs so as to obtain the layered knowledge graph.

2. The method of claim 1, wherein the obtaining historical data for a plurality of members within a community comprises:

self-history data from the community is obtained, the self-history data comprising attribute data describing the attributes of each member within the community and at least one knowledge document maintained by that member.

3. The method of claim 2, wherein the obtaining historical data for a plurality of members within a community further comprises:

external history data from the third party is obtained, the external history data including usage log data of the third party software for each member.

4. The method of claim 2, wherein the constructing a member portrayal tag for each member based on the historical data of the plurality of members comprises:

analyzing the knowledge documents of the plurality of members using a natural language processing model;

adding corresponding category labels to the knowledge documents of the plurality of members based on the analysis results of the natural language processing model; and

And constructing respective member portrait labels for the plurality of members based on the category labels and the attribute data of the plurality of members.

5. The method of claim 1, wherein the hierarchically classifying the member portrayal tags of the plurality of members comprises:

dividing member portrait labels of the members into technical class labels and non-technical class labels by using a clustering algorithm; and

and classifying the technical labels and the non-technical labels by using a deep learning algorithm to obtain the subdivision technical labels and the subdivision non-technical labels.

6. The method of any of claims 1 to 5, wherein the extracting the user's point of interest from the retrieved information comprises:

determining a text string from the retrieved information;

word segmentation is carried out on the character strings to obtain one or more words;

extracting points of interest of the user from the one or more words using a point of interest model.

7. The method of any one of claims 1 to 5, wherein the determining, according to the focus of the user and a pre-constructed hierarchical knowledge-graph, target knowledge corresponding to the focus of the user in the hierarchical knowledge-graph includes:

Determining associated entities in the hierarchical knowledge graph according to the attention points of the user;

and determining target knowledge corresponding to the attention point of the user according to the type label of the knowledge related to the associated entity.

8. The method of any of claims 1 to 5, wherein the pushing the target knowledge to the user device as the target content comprises:

sorting the target knowledge according to a sorting rule; and

pushing the ordered target knowledge to the user equipment.

9. The method of claim 8, wherein the ordering rules comprise:

sorting from high to low according to the correlation between the target knowledge and the attention points of the user;

sequencing from high to low according to the content quality of the target knowledge; or alternatively

And sorting according to the sequence selected by the user.

10. A computer device, comprising:

a memory, a processor and a computer program stored on the memory,

wherein the processor is configured to execute the computer program to implement the steps of the method of any of claims 1-9.

11. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-9.