CN107016072B - Knowledge inference system and method based on social network knowledge graph - Google Patents
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Abstract
The invention discloses a knowledge reasoning system and a knowledge reasoning method based on a social network knowledge graph. According to the method, more social relations can be found through the knowledge graph of the social network, the implicit relations in the knowledge graph of the social network are searched as required, and the graph model is updated in a regular incremental mode according to the algorithm model to obtain a more innovative and accurate relation model.
Description
Technical Field
The invention relates to a knowledge inference system and a knowledge inference method, in particular to a knowledge inference system and a knowledge inference method based on a social network knowledge graph.
Background
The conventional analysis method based on the social network knowledge graph is to analyze multiple graphs based on three basic data sources, namely an entity library, an attribute library and a relation library, and then to discover knowledge information in the graphs through the knowledge graph. The entity library, the attribute library and the relation library belong to basic databases, although the data size is huge and the types are rich. Due to the large amount and the large variety of factors, specific and useful graphic data cannot be found out as required, efficient and useful atlas models are difficult to produce, and the analysis accuracy of the corresponding relation method is low. The entity library, the attribute library and the relation library are all relatively explicit graphic data, some common graphic data can be directly inquired, and a plurality of innovative graphic modes are lacked. Many implicit graphic data information must be analyzed through a specific algorithm, and often the implicit graphic data information is core important information.
Disclosure of Invention
The invention aims to solve the technical problem of providing a knowledge inference system and a knowledge inference method based on a social network knowledge graph, which can find more social relationships through the knowledge graph of the social network, search the implicit relationship in the knowledge graph of the social network as required, and update the regular increment of a graph model according to an algorithm model to obtain a relationship model with innovation and accuracy.
The invention solves the technical problems through the following technical scheme: a knowledge inference system based on a social network knowledge graph comprises a data source module, a multi-graph network engine module, a knowledge graph discovery module, a distributed graph database module and a core sub-algorithm module, wherein the data source module, the knowledge graph discovery module, the distributed graph database module and the core sub-algorithm module are all connected with the multi-graph network engine module, and the distributed graph database module is connected with the core sub-algorithm module.
Preferably, the data source module comprises an entity library, an attribute library and a relationship library in the data of the social network, and the data of the three data sources support the basic knowledge graph of the whole social network together.
Preferably, the multiple graph network engine module sorts the data according to the data information of the data source, the person entity and the attribute entity of the social network, and the relationship between the entities to form a multiple complex network type relationship network.
Preferably, the knowledge graph discovering module analyzes and discovers a knowledge graph with a certain value according to a series of methods based on multiple complex network type relational networks generated by multiple network engines.
Preferably, the distributed graphic database module builds new graphic mode data generated by knowledge according to mining and the graph model information of regular increment because of graphic data formed by mass data passing through a multi-graph network engine.
Preferably, the core sub-algorithm module comprises a frequent behavior discovery module, a data resonance association module, an income level discovery module, a community discovery module, a group key character discovery module and other gas-increasable modules, wherein the frequent behavior discovery module is connected with the data resonance association module, the community discovery module is positioned between the data resonance association module and the community discovery module, the community discovery module is connected with the group key character discovery module, the group key character discovery module is connected with the other gas-increasable modules, implicit relations required in the social network knowledge graph are discovered through the modules, the algorithm module is an innovative algorithm model, the gas accuracy is very high, and specific graph relations can be accurately located.
The invention also provides a knowledge inference method based on the social network knowledge graph, which comprises the following steps:
step one, constructing a multiple relation graph model; constructing a multiple relation graph model according to the character entities and the attribute entities of the social network and the relation between the entities, and persistently storing the graph model by using a distributed graph database;
judging whether a knowledge graph reasoning process needs to be carried out or not; when the query condition is input, the system firstly queries the distributed graph database, if the corresponding edge of the condition is complete, the step five is carried out, otherwise, the step three is carried out, and when the same query condition is input again, the step five is carried out, so that the efficiency of the system is greatly improved;
thirdly, reasoning the knowledge graph; respectively calling knowledge graph reasoning modules corresponding to incomplete reasoning sides in a multi-graph network engine according to the completeness of the knowledge reasoning sides of the query conditions returned by the distributed graph database, and searching corresponding knowledge information in a graph model;
updating the graph model in increments at regular intervals; updating the regular increment of the image model of the distributed image database according to the edge deduced by the knowledge graph analysis reasoning process, so that the edge of the image model is more and more abundant and perfect;
and step five, ending.
Preferably, the third step comprises the following steps:
thirty, frequent behavior discovery; finding the input frequent behaviors of the searched person according to a frequent pattern mining algorithm;
thirty-one, data resonance association; based on the similarity of the tracks, useful unknown information is obtained through screening and analyzing the known tracks and some related information;
step thirty-two, income level is found; extracting economic condition related information from a multiple graph network engine, modeling aiming at the economic condition of a target character, presuming the recent economic condition of the target character according to the famous assets, consumption behaviors and social aid conditions, adopting a multi-classification algorithm, classifying all people into three grades of high, medium and low according to the recent economic condition, and finding the income level of the searched people;
thirty-three, community discovery; analyzing the boundary weight of the graph model by using a common clustering algorithm to obtain each group of nodes with the shortest weight, wherein each group is a team;
thirty-four steps, discovering key characters of a team; calculating the degree of each node of each team, wherein the node with the maximum degree is a key figure of the team;
thirty-five steps, an algorithm model can be expanded; and designing an algorithm model according to the change of the demand and then adding the algorithm model into the knowledge graph to obtain a related graph relation.
The positive progress effects of the invention are as follows: the invention can be updated along with the regular increment of the graph model, so that the whole graph database is more and more abundant; the invention obtains more valuable data information through more innovative and more accurate graph relation; according to the invention, a specific algorithm model is added to find more and more useful relations in the knowledge graph, and the expansion of the algorithm model exposes more implicit relations.
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FIG. 1 is a schematic structural diagram of the present invention.
FIG. 2 is a flow chart of the present invention.
Detailed Description
The following provides a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings.
As shown in figure 1, the knowledge inference system based on the social network knowledge graph comprises a data source module, a multiple graph network engine module, a knowledge graph discovery module, a distributed graph database module and a core sub-algorithm module, wherein the data source module, the knowledge graph discovery module, the distributed graph database module and the core sub-algorithm module are all connected with the multiple graph network engine module, and the distributed graph database module is connected with the core sub-algorithm module.
The data source module comprises an entity library, an attribute library and a relation library in the data of the social network, and the data of the three data sources jointly support a basic knowledge graph of the whole social network.
The multiple graph network engine module arranges the data according to the data information of the data source, the character entity and the attribute entity of the social network and the relationship between the entities to form a multiple complex network type relationship network.
The knowledge graph discovering module is used for analyzing and discovering a knowledge graph with certain value according to a series of methods based on multiple complex network type relational networks generated by multiple network engines.
The distributed graphic database module builds new graphic mode data generated by knowledge and regularly incremental graphic model information according to mining and the graphic data formed by mass data passing through a multi-graph network engine.
The core sub-algorithm module comprises a frequent behavior discovery module, a data resonance association module, a income level discovery module, a community discovery module, a group key character discovery module and other gas-increasable modules, wherein the frequent behavior discovery module is connected with the data resonance association module, the community discovery module is positioned between the data resonance association module and the community discovery module, the community discovery module is connected with the group key character discovery module, the group key character discovery module is connected with the gas-increasable modules, and implicit relations required in the social network knowledge graph are discovered through the modules.
As shown in FIG. 2, the knowledge inference method based on social network knowledge graph of the present invention comprises the following steps:
step one, constructing a multiple relation graph model; constructing a multiple relation graph model according to the character entities and the attribute entities of the social network and the relation between the entities, and persistently storing the graph model by using a distributed graph database;
judging whether a knowledge graph reasoning process needs to be carried out or not; when the query condition is input, the system firstly queries the distributed graph database, if the corresponding edge of the condition is complete, the step five is carried out, otherwise, the step three is carried out, and when the same query condition is input again, the step five is carried out, so that the efficiency of the system is greatly improved;
thirdly, reasoning the knowledge graph; respectively calling knowledge graph reasoning modules corresponding to incomplete reasoning sides in a multi-graph network engine according to the completeness of the knowledge reasoning sides of the query conditions returned by the distributed graph database, and searching corresponding knowledge information in a graph model;
updating the graph model in increments at regular intervals; updating the regular increment of the image model of the distributed image database according to the edge deduced by the knowledge graph analysis reasoning process, so that the edge of the image model is more and more abundant and perfect;
and step five, ending.
The third step comprises the following steps:
thirty, frequent behavior discovery; finding the input frequent behaviors of the searched person according to a frequent pattern mining algorithm, such as the most frequently-occurring place, and obtaining the activity track of the searched person;
thirty-one, data resonance association; based on the track similarity, useful unknown information is obtained through screening and analyzing the known track and some related information, for example, communication information such as a mobile phone number of a searched person is obtained through the combination of the known MAC address and vehicle data and inference;
step thirty-two, income level is found; extracting economic condition related information from a multiple graph network engine, modeling aiming at the economic condition of a target character, presuming the recent economic condition of the target character according to the famous assets, consumption behaviors, social rescue conditions and the like, adopting a multi-classification algorithm, classifying all people into three grades of high, medium and low according to the recent economic condition, and finding the income level of the searched people;
thirty-three, community discovery; analyzing the boundary weight of the graph model by using a common clustering algorithm to obtain each group of nodes with the shortest weight, wherein each group is a team;
thirty-four steps, discovering key characters of a team; calculating the degree of each node of each team, wherein the node with the maximum degree is a key figure of the team;
thirty-five steps, an algorithm model can be expanded; and designing an algorithm model according to the change of the demand and then adding the algorithm model into the knowledge graph to obtain a related graph relation.
The above embodiments are described in further detail to solve the technical problems, technical solutions and advantages of the present invention, and it should be understood that the above embodiments are only examples of the present invention and are not intended to limit the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A knowledge inference system based on social network knowledge graph is characterized by comprising a data source module, a multiple graph network engine module, a knowledge graph discovery module, a distributed graph database module and a core sub-algorithm module, wherein the data source module, the knowledge graph discovery module, the distributed graph database module and the core sub-algorithm module are all connected with the multiple graph network engine module, and the distributed graph database module is connected with the core sub-algorithm module;
the core sub-algorithm module comprises a frequent behavior discovery module, a data resonance association module, a income level discovery module, a community discovery module and a group key character discovery module, wherein the frequent behavior discovery module is connected with the data resonance association module, the community discovery module is positioned between the data resonance association module and the community discovery module, the community discovery module is connected with the group key character discovery module, and the implicit relation required in the social network knowledge graph is discovered through the modules.
2. The social network knowledge graph-based reasoning system of claim 1, wherein the data source module comprises an entity library, an attribute library and a relationship library in the data of the social network, and the data of the three data sources jointly support the basic knowledge graph of the whole social network.
3. The social network knowledge graph-based reasoning system of claim 1, wherein the multiple graph network engine module is configured to organize data according to data information of data sources and human entities and attribute entities of the social network and relationships between the entities to form a multiple complex network relationship network.
4. The social network knowledge graph-based reasoning system of claim 1, wherein the knowledge graph discovery module is configured to discover a knowledge graph of value based on analysis of multiple complex networked relationship networks generated by multiple network engines.
5. The social network knowledge graph-based reasoning system of claim 1, wherein the distributed graph database module generates new graph schema data and periodically incremental graph model information based on mining building knowledge from graph data formed by mass data passing through a multiple graph network engine.
6. A knowledge inference method based on a social network knowledge graph is characterized by comprising the following steps:
step one, constructing a multiple relation graph model; constructing a multiple relation graph model according to the character entities and the attribute entities of the social network and the relation between the entities, and persistently storing the graph model by using a distributed graph database;
judging whether a knowledge graph reasoning process needs to be carried out or not; when the query condition is input, the system firstly queries the distributed graph database, if the corresponding edge of the condition is complete, the step five is carried out, otherwise, the step three is carried out, and when the same query condition is input again, the step five is carried out, so that the efficiency of the system is greatly improved;
thirdly, reasoning the knowledge graph; respectively calling knowledge graph reasoning modules corresponding to incomplete reasoning sides in a multi-graph network engine according to the completeness of the knowledge reasoning sides of the query conditions returned by the distributed graph database, and searching corresponding knowledge information in a graph model;
updating the graph model in increments at regular intervals; updating the regular increment of the image model of the distributed image database according to the edge deduced by the knowledge graph analysis reasoning process, so that the edge of the image model is more and more abundant and perfect;
and step five, ending.
7. A social network knowledge graph-based reasoning method according to claim 6, wherein said step three comprises the steps of:
thirty, frequent behavior discovery; finding the input frequent behaviors of the searched person according to a frequent pattern mining algorithm;
thirty-one, data resonance association; based on the similarity of the tracks, useful unknown information is obtained through screening and analyzing the known tracks and some related information;
step thirty-two, income level is found; extracting economic condition related information from a multiple graph network engine, modeling aiming at the economic condition of a target character, presuming the recent economic condition of the target character according to the famous assets, consumption behaviors and social aid conditions, adopting a multi-classification algorithm, classifying all people into three grades of high, medium and low according to the recent economic condition, and finding the income level of the searched people;
thirty-three, community discovery; analyzing the boundary weight of the graph model by using a common clustering algorithm to obtain each group of nodes with the shortest weight, wherein each group is a team;
thirty-four steps, discovering key characters of a team; calculating the degree of each node of each team, wherein the node with the maximum degree is a key figure of the team;
thirty-five steps, an algorithm model can be expanded; and designing an algorithm model according to the change of the demand and then adding the algorithm model into the knowledge graph to obtain a related graph relation.
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| CN107704572B (en) * | 2017-09-30 | 2021-07-13 | 北京奇虎科技有限公司 | Method and device for mining creation angle of character entity |
| CN108197108A (en) * | 2017-12-29 | 2018-06-22 | 智搜天机（北京）信息技术有限公司 | The method and its system that terminal contact based on AI intelligently extends |
| CN108959370B (en) * | 2018-05-23 | 2021-04-06 | 哈尔滨工业大学 | Community discovery method and device based on entity similarity in knowledge graph |
| CN108984592B (en) * | 2018-05-31 | 2020-11-20 | 中诚信征信有限公司 | Data processing system, method and device based on graphic database |
| CN109471959B (en) * | 2018-06-15 | 2022-06-14 | 中山大学 | Figure reasoning model-based method and system for identifying social relationship of people in image |
| CN109189937B (en) * | 2018-08-22 | 2021-02-09 | 创新先进技术有限公司 | Feature relationship recommendation method and device, computing device and storage medium |
| CN109359199A (en) * | 2018-08-27 | 2019-02-19 | 平安科技（深圳）有限公司 | Fund manager's group dividing method, system, computer equipment and storage medium |
| CN109299334B (en) * | 2018-08-31 | 2021-06-04 | 杭州振牛信息科技有限公司 | Data processing method and device of knowledge graph |
| CN109271478B (en) * | 2018-09-17 | 2021-07-27 | 华中科技大学 | Knowledge graph data layout method in social network based on BFS forest |
| CN111104609B (en) * | 2018-10-26 | 2023-10-10 | 百度在线网络技术（北京）有限公司 | Inter-person relationship prediction method, inter-person relationship prediction device, and storage medium |
| CN110110155B (en) * | 2019-04-03 | 2021-01-26 | 中国人民解放军战略支援部队信息工程大学 | Character knowledge graph attribute acquisition method and device based on meta-social relationship circle |
| CN110781308B (en) * | 2019-06-25 | 2023-08-29 | 上海旭荣网络科技有限公司 | Anti-fraud system for constructing knowledge graph based on big data |
| CN110390039A (en) * | 2019-07-25 | 2019-10-29 | 广州汇智通信技术有限公司 | Social networks analysis method, device and the equipment of knowledge based map |
| CN110737702A (en) * | 2019-10-22 | 2020-01-31 | 北京明略软件***有限公司 | Social relationship analysis method and device, computer equipment and readable storage medium |
| CN110852895A (en) * | 2019-11-13 | 2020-02-28 | 易联众信息技术股份有限公司 | Medical security cheat-insurance behavior discovery method based on knowledge graph |
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