CN111898760A - Knowledge inference method and system based on knowledge graph path analysis - Google Patents
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Abstract
In order to solve the problem of low knowledge inference efficiency in the prior art, the knowledge inference method based on the knowledge graph path analysis is provided, and the knowledge inference efficiency is improved. The knowledge inference method based on the knowledge graph path analysis comprises the following steps: acquiring nodes to be expanded according to node query information input by a user; carrying out multi-dimensional expansion according to the nodes to be expanded so as to generate knowledge graph subgraphs of the nodes to be expanded; acquiring nodes to be inferred of the knowledge graph subgraph selected by a user; and carrying out map path analysis on the node to be inferred to generate a knowledge map path of the node to be inferred. The application also discloses a corresponding knowledge reasoning system and computer equipment based on the knowledge graph path analysis, which can improve the knowledge reasoning efficiency.
Description
Technical Field
The disclosure relates to the technical field of data mining, in particular to a knowledge reasoning method and a knowledge reasoning system based on knowledge graph path analysis.
Background
The knowledge graph is a modern theory which achieves the aim of multi-discipline fusion by combining theories and methods of applying subjects such as mathematics, graphics, information visualization technology, information science and the like with methods such as metrology introduction analysis, co-occurrence analysis and the like and utilizing a visualized graph to vividly display the core structure, development history, frontier field and overall knowledge framework of the subjects. The method displays the complex knowledge field through data mining, information processing, knowledge metering, knowledge reasoning and graph drawing, reveals the dynamic development rule of the knowledge field, and provides a practical and valuable reference for subject research. The current knowledge inference method based on knowledge graph analysis generally analyzes multiple graphs based on three basic data sources of an entity library, an attribute library and a relation library, and then discovers knowledge information in the multiple graphs through a knowledge graph. The entity library, the attribute library and the relation library belong to basic databases, and have huge data volume and rich types, but the entity library, the attribute library and the relation library have large quantities and multiple types, so that specific and useful graphic data cannot be found out as required, and the knowledge reasoning efficiency is low.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present disclosure provides a knowledge inference method and system based on knowledge graph path analysis, which improve the efficiency of knowledge inference.
In a first aspect of the disclosure, a knowledge inference method based on knowledge graph path analysis includes:
acquiring nodes to be expanded according to node query information input by a user;
carrying out multidimensional expansion according to the nodes to be expanded so as to generate knowledge graph subgraphs of the nodes to be expanded;
acquiring a node to be inferred of the knowledge graph subgraph selected by a user;
and carrying out map path analysis on the node to be inferred to generate a knowledge map path of the node to be inferred.
Optionally, the graph path analysis includes a shortest path analysis.
Optionally, the atlas path analysis includes all path analyses.
Optionally, the method further includes generating a path search identifier, where the path search identifier includes an analysis dimension, an analysis type, an analysis range, a node attribute, and a node relationship attribute of preset graph path analysis.
Optionally, the analysis range of the graph path analysis includes knowledge graph subgraphs of the nodes to be expanded.
Optionally, the analysis type of the graph path analysis includes all path analysis or shortest path analysis.
Optionally, performing graph path analysis on the node to be inferred includes:
when the node relation involved in the graph path analysis is larger than a first set value or the node relation is larger than a second set value, exporting full graph data of a knowledge graph or subgraph data of a node to be inferred into a node and a relation file by using Spark graph X, storing the node and the relation file into an HDFS, packaging Cypher statements of the knowledge graph into a graph X method according to the analysis type of the graph path analysis, and submitting the graph X method to a CDH cluster for execution.
In a second aspect of the present disclosure, a knowledge inference system based on knowledge graph path analysis includes:
the first node acquisition module is used for acquiring nodes to be expanded according to node query information input by a user;
the subgraph generation module is used for carrying out multidimensional expansion according to the nodes to be expanded so as to generate the knowledge graph subgraph of the nodes to be expanded;
the second node acquisition module is used for acquiring the nodes to be inferred of the knowledge graph subgraph selected by the user;
and the path generation module is used for carrying out map path analysis on the node to be inferred so as to generate a knowledge map path of the node to be inferred.
Optionally, the system further includes:
the path searching module is used for generating path searching identifiers, wherein the path searching identifiers comprise preset analysis dimensions, analysis types, analysis ranges, node attributes and node relation attributes of map path analysis.
In a third aspect of the disclosure, a computer device comprises a processor and a memory, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the method of any one of the first aspect of the disclosure.
The technical scheme of the present disclosure can be implemented to obtain the following beneficial technical effects: the method comprises the steps of generating a knowledge graph subgraph of a node to be expanded according to node query information input by a user, carrying out graph path analysis according to the knowledge graph subgraph to generate a knowledge graph path, expanding the node concerned by the user to generate the knowledge graph subgraph, carrying out path analysis based on the knowledge graph subgraph and generating the knowledge graph path, further forming useful graph data, and improving knowledge reasoning efficiency.
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The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
FIG. 1 is a flow diagram of a method of knowledge inference based on knowledge-graph path analysis in an embodiment of the present disclosure;
fig. 2 is a block diagram of a knowledge inference system based on knowledge-graph path analysis in an embodiment of the present disclosure.
Detailed Description
The present disclosure will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the present disclosure are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, a knowledge inference method based on knowledge graph path analysis includes:
step S1: acquiring nodes to be expanded according to node query information input by a user;
step S2: carrying out multidimensional expansion according to the nodes to be expanded so as to generate knowledge graph subgraphs of the nodes to be expanded;
step S3: acquiring a node to be inferred of the knowledge graph subgraph selected by a user;
step S4: and carrying out map path analysis on the node to be inferred to generate a knowledge map path of the node to be inferred.
The execution main body of the method can be a computer, and the execution main body is taken as the computer for explanation, firstly, a user inputs node query information to the computer, the computer receives the node query information input by the user, and queries corresponding nodes according to the node query information to obtain nodes to be expanded; performing multi-dimensional expansion according to the nodes to be expanded to generate knowledge graph subgraphs of the nodes to be expanded; and then, the user selects corresponding nodes from the knowledge graph sub-graph as nodes to be inferred, the computer acquires the nodes to be inferred, and generates knowledge graph paths of the nodes to be inferred in a graph path analysis mode, so that useful graph data are formed, and the knowledge inference efficiency is improved.
The multi-dimensional expansion refers to expansion of multiple analysis dimensions; taking the node as a three-country character as an example, when the node to be expanded is Liu Stan, the method comprises the following steps: the jie bai brother of Liu Bei is mourning and Zhang fei, which is a one-dimensional expansion (the relationship attribute of the one-dimensional expansion is the jie bai brother), and the direct relatives of mourning include Guanxing, Guansuo, Guanfeng and Guanping; this is the one-dimensional expansion of feather closing, that is, the two-dimensional expansion of Liu Bei (the relationship attribute of the two-dimensional expansion is the direct relatives); the Guanping wife is Zhao, which is a one-dimensional expansion of Guanping, a two-dimensional expansion of Guanyu, and a three-dimensional expansion of Liu Bei. That is, Zhao is the wife of Liu Bei who has a brother of Bai Jie.
The knowledge graph subgraph of the node to be expanded uses a knowledge graph associated with the node to be expanded, takes the node as a three-country character as an example, and if the node to be expanded is a prepare, the steps are as follows: the knot and brave brother of Liu Bei is a knowledge map subgraph of Guanyu and Zhang Fei; the direct relatives of the guan Yu comprise guan, guan suo, guan Feng and guan ping, namely knowledge map subgraphs. The knowledge graph subgraph of the nodes to be expanded takes the nodes to be expanded as a starting point, and expands the relation with the nodes to be expanded, so that a user can conveniently select corresponding nodes from the nodes to be expanded as the nodes to be inferred; if the user selects the nodes of the knowledge map subgraph as Zhang Fei and Zhao, the computer finds out the shortest path of Zhang Fei and Zhao according to the map path analysis, namely Zhao is a wife who is a son of Zhang Fei brother of Zhang; after the shortest path is generated, the user can visually observe the relationship between Zhang Fei and Zhao, so that useful graphic data are formed, and the knowledge reasoning efficiency is improved.
According to the method, the knowledge graph subgraph of the nodes to be expanded can be generated according to the node query information input by the user, graph path analysis is carried out according to the knowledge graph subgraph to generate a knowledge graph path, the nodes concerned by the user are expanded to generate the knowledge graph subgraph, path analysis is carried out on the basis of the knowledge graph subgraph to generate the knowledge graph path, and the knowledge inference efficiency of the knowledge graph is greatly improved.
As can be appreciated, graph path analysis includes all path analysis, Dijkstra shortest path analysis, maximum spanning tree analysis, single shortest path analysis, full graph shortest path analysis, random walk analysis, all shortest path analysis, and minimum spanning tree analysis, among others.
The single shortest path analysis is to calculate paths between a node and all other nodes and sum values (weights of relationships such as cost, distance, time or capacity) of the paths and all other nodes, and obtain a path with the minimum sum; the full-graph shortest path analysis is to calculate a shortest path forest containing all shortest paths between nodes in the graph, and when the shortest paths are blocked or become suboptimal, the shortest paths are switched to new shortest paths; the small spanning tree analysis is a path for computing a minimum value (e.g., weight of a relationship such as cost, time or capacity) associated with all nodes in the access tree, and is used for approximating some NP-hard problems such as the traveling quotient problem and random or iterative rounding.
Specifically, the graph path analysis may be a shortest path analysis.
Taking the node as a three-country character as an example, when the node to be expanded is Liu Bei, if the user selects the node of the knowledge graph subgraph as Zhang Fei and Zhao, the computer finds out the shortest path of Zhang Fei and Zhao according to graph path analysis, namely Zhao is a wife of a son of Zhang Fei, the son of Zhang Bai brother; after the shortest path is generated, the user can visually observe the relationship between Zhang Fei and Zhao, useful graphic data is formed, and the knowledge reasoning efficiency is improved.
Specifically, the shortest path analysis may be a Dijkstra (Dijkstra) algorithm-based shortest path analysis. The shortest path analysis calculates the shortest path from one node to all other nodes; expanding towards the outer layer by taking the starting point as a center until the expansion reaches the end point; and then obtaining the optimal solution of the shortest path.
Specifically, the map path analysis may be all path analysis; and the all-path analysis comprises the step of calculating all relation paths which possibly exist between two nodes to be inferred according to the two nodes to be inferred of the knowledge graph subgraph selected by the user.
Specifically, the graph path analysis may be a maximum spanning tree analysis; the maximum spanning tree analysis includes computing a maximum path associated with all nodes in the access tree.
Specifically, the map path analysis may be a random walk analysis; the random walk analysis includes starting with a node to be reasoned, randomly selecting a neighbor or navigating to the neighbor according to a provided probability distribution, then navigating from the node to be reasoned, and retaining the resulting path in a list.
Specifically, the graph path analysis may be a minimum spanning tree analysis; the minimum spanning tree analysis includes computing a minimum path associated with all nodes in the access tree.
In an optional embodiment, the method further includes generating a path search identifier, where the path search identifier includes an analysis dimension, an analysis type, an analysis range, a node attribute, and a node relationship attribute of a preset graph path analysis.
Specifically, the analysis dimension of the map path analysis may be one-dimensional, two-dimensional, three-dimensional, and the like.
The analysis type of the graph path analysis may be all path analysis or shortest path analysis, etc.
The analysis range of the graph path analysis can be a full knowledge graph and a knowledge graph subgraph of the nodes to be expanded.
The node attributes of the graph path analysis may be male, female, etc.
The node relationship attributes of the graph path analysis can be directly related relatives, the family brothers, and the like.
The path searching identifier can be repeatedly used, so that the purpose of limiting conditions of analysis of rapid fixed-spectrum path analysis is achieved, and convenience of user operation is improved; the node attribute and the node relation attribute can be selected according to requirements without limitation.
In an optional embodiment, performing graph path analysis on the node to be inferred includes:
when the node relation involved in the graph path analysis is larger than a first set value or the node relation is larger than a second set value, exporting full graph data of a knowledge graph or subgraph data of a node to be inferred into a node and a relation file by using Spark GraphX, and storing the node and the relation file into an HDFS; and packaging Cypher statements of the knowledge graph into a GraphX method according to the analysis type of the graph path analysis, and submitting the GraphX method to a CDH cluster for execution.
It can be known that the CDH cluster is based on the nodes and the relationship files in the HDFS when executing the GraphX method. It can be known that, when the node relationship related to the graph path analysis is less than or equal to a first set value and the node relationship is less than or equal to a second set value, it is not necessary to export the full graph data of the knowledge graph or the subgraph data of the knowledge graph of the node to be inferred into a node and relationship file, and it is also not necessary to package the Cypher statements of the analysis type knowledge graph analyzed according to the graph path into the GraphX method.
The Spark graph X is a distributed graph processing framework, provides a simple, easy-to-use and rich interface for graph calculation and graph mining based on a Spark platform, and greatly facilitates the requirement on distributed graph processing; at a high level, GraphX extends Spark-RDD by introducing a new graph abstraction: a directed multi-graph has attributes for each vertex and edge. To support graph computation, graphX discloses a set of basic operators (e.g., subgraphs, joinVertics, and aggregatEessages) as well as optimized variants of the Pregel API. In addition, GraphX includes more and more graphics algorithms and builders to simplify the graphics analysis task.
The HDFS described above is a Hadoop distributed file system, i.e., a distributed file system designed to run on general purpose hardware.
The CDH is the abbreviation of Cloudera's Distribution incorporation Apache Hadoop.
The Cypher is a descriptive graph query language, and refers to a Neo4j database operation statement by using sql statement.
The first setting value and the second setting value may be set as required, for example, the first setting value is set to 10000, and the second setting value is set to 5000.
When a user executes complex graph analysis (when the default is that the node involved in the analysis is larger than 10000 or the relationship is larger than 5000), the graph analysis is executed by adopting a big data solution, firstly, the analyzed knowledge graph subgraph or full graph data is exported to be the node, the relationship file is stored in an HDFS (Hadoop distributed file system), then, the Cypher statement of the knowledge graph is packaged to be a GraphX method according to the analysis type and submitted to a CDH (central data processing) cluster for execution so as to realize the knowledge graph analysis, and the analysis result is exported to the designated position of the HDFS so as to be convenient for a client to read the result. Based on the characteristics of Spark memory calculation, the map analysis efficiency is greatly improved by changing the scheme under the background of big data, and the operation efficiency is more than 10 times of that of the traditional Cypher analysis.
Referring to fig. 2, a knowledge-graph based reasoning system includes:
the first node acquisition module 1 is used for acquiring nodes to be expanded according to node query information input by a user;
the subgraph generation module 2 is used for carrying out multidimensional expansion according to the nodes to be expanded so as to generate knowledge graph subgraphs of the nodes to be expanded;
the second node acquisition module 3 is used for acquiring the nodes to be inferred of the knowledge graph subgraph selected by the user;
and the path generating module 4 is used for performing graph path analysis on the node to be inferred so as to generate a knowledge graph path of the node to be inferred.
Referring to fig. 2, the knowledge-graph based reasoning system further comprises:
the path searching module is used for generating path searching identifiers, wherein the path searching identifiers comprise preset analysis dimensions, analysis types, analysis ranges, node attributes and node relation attributes of map path analysis.
Specifically, the analysis dimension of the map path analysis may be one-dimensional, two-dimensional, three-dimensional, and the like.
The analysis type of the graph path analysis may be all path analysis or shortest path analysis, etc.
The analysis range of the graph path analysis can be a full knowledge graph and a knowledge graph subgraph of the nodes to be expanded.
The node attributes of the graph path analysis may be male, female, etc.
The node relationship attributes of the graph path analysis can be directly related relatives, the family brothers, and the like.
As can be appreciated, graph path analysis includes all path analysis, Dijkstra shortest path analysis, maximum spanning tree analysis, single shortest path analysis, full graph shortest path analysis, random walk analysis, all shortest path analysis, and minimum spanning tree analysis, among others.
Specifically, the graph path analysis may be a shortest path analysis.
Specifically, the shortest path analysis may be a shortest path analysis based on Dijkstra (Dijkstra) algorithm. The shortest path analysis calculates the shortest path from one node to all other nodes; expanding towards the outer layer by taking the starting point as a center until the expansion reaches the end point; and then obtaining the optimal solution of the shortest path.
Specifically, the map path analysis may be all path analysis; and the all-path analysis comprises the step of calculating all relation paths which possibly exist between two nodes to be inferred according to the two nodes to be inferred of the knowledge graph subgraph selected by the user.
Specifically, the graph path analysis may be a maximum spanning tree analysis; the maximum spanning tree analysis includes computing a maximum path associated with all nodes in the access tree.
Specifically, the map path analysis may be a random walk analysis; the random walk analysis includes starting with a node to be reasoned, randomly selecting a neighbor or navigating to the neighbor according to a provided probability distribution, then navigating from the node to be reasoned, and retaining the resulting path in a list.
Specifically, the graph path analysis may be a minimum spanning tree analysis; the minimum spanning tree analysis includes computing a minimum path associated with all nodes in the access tree.
In an optional embodiment, performing graph path analysis on the node to be inferred includes:
when the node relation involved in the graph path analysis is larger than a first set value or the node relation is larger than a second set value, exporting full graph data of a knowledge graph or subgraph data of a node to be inferred into a node and a relation file by using Spark graph X, storing the node and the relation file into an HDFS, packaging Cypher statements of the knowledge graph into a graph X method according to the analysis type of the graph path analysis, and submitting the graph X method to a CDH cluster for execution.
The embodiment also discloses a computer device, which comprises a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement any one of the knowledge inference methods based on the knowledge graph path analysis.
In the description herein, reference to the description of the terms "one embodiment/mode," "some embodiments/modes," "example," "specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/aspects or examples and features of the various embodiments/aspects or examples described in this specification can be combined and combined by one skilled in the art without conflicting therewith.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It will be understood by those skilled in the art that the foregoing embodiments are provided merely for clarity of disclosure and are not intended to limit the scope of the disclosure. Other variations or modifications may occur to those skilled in the art, based on the foregoing disclosure, and are within the scope of the present disclosure.
Claims (10)
1. A knowledge inference method based on knowledge graph path analysis is characterized by comprising the following steps:
acquiring nodes to be expanded according to node query information input by a user;
carrying out multidimensional expansion according to the nodes to be expanded so as to generate knowledge graph subgraphs of the nodes to be expanded;
acquiring a node to be inferred of the knowledge graph subgraph selected by a user;
and carrying out map path analysis on the node to be inferred to generate a knowledge map path of the node to be inferred.
2. The method of intellectual inference based on knowledge graph path analysis as claimed in claim 1, wherein the graph path analysis includes a shortest path analysis.
3. The method of intellectual inference based on knowledge graph path analysis according to claim 1 wherein the graph path analysis includes all path analysis.
4. The method of intellectual inference based on knowledgegraph path analysis according to claim 1, characterized in that the method further comprises generating path search flags, wherein the path search flags each contain analysis dimension, analysis type, analysis range, node attribute and node relationship attribute of preset graph path analysis.
5. The method of intellectual inference based on knowledge graph path analysis according to claim 4, characterized in that the analysis scope of the graph path analysis includes knowledge graph subgraphs of the nodes to be extended.
6. The method of intellectual inference based on knowledge graph path analysis as claimed in claim 4, wherein the analysis type of the graph path analysis includes all path analysis or shortest path analysis.
7. The knowledge inference method based on knowledge graph path analysis according to claim 1, wherein the graph path analysis is performed on the node to be inferred, and comprises:
when the node relation involved in the graph path analysis is larger than a first set value or the node relation is larger than a second set value, exporting the full graph data of the knowledge graph or the subgraph data of the node to be inferred into a node and relation file by using Spark graph X, storing the node and relation file into an HDFS, packaging Cypher statements of the knowledge graph into a graph X method according to the analysis type of the graph path analysis, and submitting the graph X method to a CDH cluster for execution.
8. A knowledge inference system based on knowledge graph path analysis, comprising:
the first node acquisition module is used for acquiring nodes to be expanded according to node query information input by a user;
the subgraph generation module is used for carrying out multidimensional expansion according to the nodes to be expanded so as to generate the knowledge graph subgraph of the nodes to be expanded;
the second node acquisition module is used for acquiring the nodes to be inferred of the knowledge graph subgraph selected by the user;
and the path generation module is used for carrying out map path analysis on the node to be inferred so as to generate a knowledge map path of the node to be inferred.
9. The system of knowledge inference based on knowledge graph path analysis of claim 8, wherein the system further comprises:
the path searching module is used for generating path searching identifiers, wherein the path searching identifiers comprise preset analysis dimensions, analysis types, analysis ranges, node attributes and node relation attributes of map path analysis.
10. A computer device comprising a processor and a memory, said memory having stored therein at least one instruction that is loaded and executed by said processor to implement the method of any of claims 1 to 7.
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