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Event mode frequent subgraph mining and prediction method

A technology of frequent subgraphs and prediction methods, applied in the field of knowledge engineering, can solve problems such as non-data, achieve the effect of rapid mining, reduce mining overhead, and promote the level of cognitive reasoning

Active Publication Date: 2021-01-29
10TH RES INST OF CETC
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Problems solved by technology

Inspired by the DBSCAN algorithm, a certain core object is also used to represent several data points, but the core object is a virtual point, not the actual input data

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  • Event mode frequent subgraph mining and prediction method

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Embodiment Construction

[0015] refer to figure 1 . According to the present invention, in the density-based graph summary stage and the pattern mining stage, in the density-based graph summary stage, the nodes in the density-based graph summary graph are divided into clusters or super nodes, focusing on preserving the nodes and On the edge, select nodes with higher density in the node set V in order to start summarizing, and construct a concise high-level graph; in the pattern mining stage, carry out event pattern mining based on event graphs, and perform frequent subgraph mining on large-scale event graphs , mining frequent subgraphs based on density-based graph summarization and event patterns of frequent subgraphs, that is, finding frequently occurring subgraphs in the atlas of event graphs; the density-based graph summarization algorithm summarizes the input graph G, and then uses the summarization result as Input, perform frequent subgraph mining and mining preprocessing based on graph summarie...

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Abstract

The invention discloses an event mode frequent subgraph mining and prediction method, relates to the technical field of knowledge engineering, and aims to reduce mining overhead and improve mining speed. According to the technical scheme, the method comprises the following steps: dividing nodes in a density graph abstract graph into clusters or super nodes in a density-based graph abstract stage,and sequentially selecting the nodes to construct a concise high-level graph; in the mode mining stage, carrying out frequent sub-graph mining on a large-scale event graph, mining frequent sub-graphsbased on the event mode, and finding frequently appearing sub-graphs in a graph set of the event graph; abstracting the input graph G based on a graph abstract algorithm, and performing frequent sub-graph mining and graph abstract-based mining preprocessing by taking an abstract result as input; and finally, mining and predicting an event mode from multiple sources and multiple perspectives by utilizing multi-source data, and performing candidate set filtering and frequent sub-graph output according to a minimum support degree min_sup defined by a user or other output standards.

Description

technical field [0001] The invention relates to the technical field of knowledge engineering, in particular to a method for mining and predicting frequent subgraphs of event patterns. Background technique [0002] The knowledge graph originated from Google Knowledge Graph. A knowledge graph is essentially a semantic network. Its nodes represent entities or concepts, and edges represent various semantic relationships between entities / concepts. [0003] Event graph, as a typical knowledge graph, has not been fully and effectively utilized at present, and its potential value needs to be developed and utilized urgently. The event map is mainly composed of event-related elements, including the entire process of the occurrence and development of all historical events, and also describes the succession, sequence, and causal relationship between different events. If the event map is carefully analyzed and the laws and patterns of the events contained in it are excavated, then fut...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/34G06F16/33
CPCG06F16/367G06F16/345G06F16/3331G06F2216/03Y02D10/00
Inventor 崔莹代翔戴礼灿杨露潘磊
Owner 10TH RES INST OF CETC
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