Knowledge verification model construction and analysis method based on probability soft logic

Abstract

The invention belongs to the technical field of information extraction, and particularly relates to a knowledge verification model construction and analysis method based on probability soft logic, which comprises the following steps of: a, forming a candidate knowledge set by knowledge extracted from webpage web texts of a plurality of data sources by an information extraction system; b, carryingout credibility calculation on the candidate knowledge set; c, performing logic predicate representation on each entity in the candidate knowledge set; d, constructing a first-order logic rule of theknowledge verification model based on the entity analysis and the ontology constraint, generating the first-order logic rule in the probability soft logic model through the constructed logic rule, andachieving entity relationship and entity label verification in the candidate knowledge set; and e, setting probability distribution of the knowledge verification model, and calculating and selectingcorresponding knowledge to be updated through an inference algorithm. According to the method, the candidate knowledge set is verified, so that the accuracy of the candidate knowledge set is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of information extraction, and in particular relates to a knowledge verification model construction and analysis method based on probability soft logic. Background technique [0002] With the rapid popularization of the Internet and the continuous popularization of the concept of Web3.0, building a large-scale knowledge base has become an urgent task for all walks of life. At the same time, search engines and research institutions have also released various large-scale knowledge bases. For example, large-scale multi-user online editing knowledge bases such as Wikipedia and Baidu Encyclopedia, Google's Freebase knowledge base, KnowledgeGraph, etc. [0003] The construction of knowledge graph is a dynamic process, which needs to add new knowledge and update dynamic knowledge in time to improve and supplement the existing knowledge graph. New knowledge acquisition methods often use information extraction techn...

AI Technical Summary

Problems solved by technology

There are often inconsistencies in the new knowledge extracted by the information extraction system, such as non-unique entity labels, multiple representations of entities, and uncertain entity relationships in new knowledge.

Images