Knowledge Base Completion Method Based on Parallel Rule Mining Algorithm prmatc

Abstract

The present invention relates to a knowledge base completion method based on the parallel rule mining algorithm PRMATC, comprising the following steps: Step S1: importing and storing all fact triples and entities in the large-scale semantic network knowledge base KB to the distributed cluster Neo4j In the graph database; step S2: build the BILSTM-CRF model and train it; step S3: identify and classify the entities on both sides of the relationship through the trained BILSTM-CRF model, and convert the definition domain and value domain of the relationship; step S4 : improve the FP-Growth algorithm; step S5: dig out the hidden strong association rules between transactions; step S6: convert the domain and strong association rules into Horn logic rules according to the relationship obtained; step S7: obtain the Horn logic rules according to Acquire new knowledge and add it to the knowledge base KB. The invention can efficiently find Horn rules representing the knowledge base, and at the same time, it is superior to other rule mining systems in terms of the number and accuracy of mining rules, and can better complement the knowledge base.

Description

technical field [0001] The invention relates to the field of mass data storage and reasoning under a knowledge graph, in particular to a knowledge base completion method based on a parallel rule mining algorithm PRMATC. Background technique [0002] Mining Horn rules from a large-scale semantic network knowledge base, and then using these rules to help infer and add the missing knowledge in the knowledge base is one of the extremely effective means to realize the dynamic growth of the knowledge base. Association rule mining algorithm is one of the important algorithms in the field of data mining, and its purpose is to mine the implicit relationship between transactions. Traditional algorithms include the Aprior i algorithm and the FP-Growth algorithm. The traditional association rule mining algorithm has achieved good results in small-scale data sets. However, with the rapid development of Internet technology in recent years, network data has shown explosive growth. The tra...

AI Technical Summary

Problems solved by technology

Traditional association rule mining algorithms have achieved good results in small-scale data sets. However, with the rapid development of Internet technology in recent years, network data has shown explosive growth. Traditional association rule mining algorithms cannot be calculated by a single node and run out of memory. and other problems, so that it cannot meet the needs of network big data

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