Knowledge question-answer system established on basis of Wiki semantic network, and intelligent retrieval method

Abstract

The invention discloses a knowledge question-answer system established on basis of a Wiki semantic network, and an intelligent retrieval method and relates to the technical fields such as text semantic analysis, intelligent retrieval and machine learning. According to the invention, semi-structuralized text data grasped by vertical field Wiki in an oriented manner is used; knowledge elements are extracted automatically according to a pre-defined knowledge ontology; further, a deep learning algorithm can be used to train and establish an ontology semantic comprehension model; and thus, semantic comprehension of clustering relations and inference relations of the knowledge elements could be achieved. According to the invention, advantages of machine learning including automatic theme inference based on context semantic understanding and expert knowledge oriented optimization are synthesized; matched retrieval results matched through rational semantic association and expansion as well as inferred retrieval results are returned to a retrieval word input by a user; and in combination with definition of expert knowledge, accuracy and depth of the information retrieval can be increased effectively, and knowledge navigation can be achieved. The system and method are applicable to a medical-themed mapping knowledge domain, knowledge questioning and answering and a content recommendation system.

Description

technical field [0001] The invention relates to technical fields such as text semantic analysis, intelligent retrieval and machine learning, and in particular to a knowledge question answering system and an intelligent retrieval method based on a Wiki semantic network. Background technique [0002] The amount of information in contemporary society is increasing rapidly, and the medical field is facing frequent changes in knowledge. In the face of massive medical knowledge, both new medical practitioners and senior medical experts have a high demand for professional knowledge inquiries. Traditional search engine retrieval cannot meet the rigorous, accurate and relevant requirements of professional medical knowledge retrieval from the content it covers to the retrieval technology. [0003] An effective solution for professional knowledge query needs is the industry domain ontology knowledge base. Ontology is not only the basic terms and the relationships between them, but al...

AI Technical Summary

Problems solved by technology

However, no matter which processing method is used, it only expands the scope of matching, and the semantic association rules involved are artificially set rather than automatically discovered by machines. From the perspective of processing efficiency, the processing range and speed are restricted. In the face of large databases and knowledge updates In high-frequency industries, it is almost impossible to update semantic rules in real time; from the perspective of improvement, the matching of synonyms and ambiguous words is the horizontal expansion of vocabulary semantics, and it has not realized the semantics expressed by user keywords, and cannot carry out implicit knowledge. discovery or user search intent inference

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