Tourism request-answer system answer abstracting method based on ontology reasoning

Abstract

The invention relates to an answer extraction method for a tourism question and answer system based on ontology reasoning, which belongs to the field of artificial intelligence. The answer extraction method for the tourism question and answer system based on ontology reasoning is characterized in that: firstly, semantic rules in the field are defined, an artificial ontology knowledge base is constructed, and question sentences of users are analyzed; secondly, answer extraction is performed by combing semantic rule-based reasoning and information retrieval, instead of simple matching; and thirdly, corresponding answer extraction algorithms are designed according to different question sentence types. The invention provides the answer extraction method for the tourism question and answer system based on ontology reasoning, which introduces the ontology concept into construction of the knowledge base of the question and answer system, uses an OWL (Ontology Web Language) ontology descriptive language to clearly and definitely represent concepts, attributes and relations in the field of tourism, and more effectively organizes knowledge. In an open test, the precision of answers of the question and answer system based on ontology reasoning to 1346 natural language questions of the users reaches 81.35 percent, and the recall rate reaches 90.49 percent.

Description

technical field [0001] The invention relates to an answer extraction method of a tourism question answering system based on ontology reasoning. It belongs to the field of artificial intelligence. Background technique [0002] Automatic question answering system, also known as QA (Queslion Answering) system, is a new intelligent technology. It uses natural language processing technology to complete the analysis and processing of user questions on the one hand and complete the generation of correct answers on the other hand. Get the information you want quickly and accurately in the online world. At this stage, it is still very difficult for computers to fully understand human language, but for specific fields, using targeted methods, many successful application cases have been developed. [0003] In the automatic question answering system in the limited field, the answer extraction part is a difficult point, which is related to the final effect of the entire question answer...

AI Technical Summary

Problems solved by technology

[0007] At the same time, the current question answering system lacks reasoning ability, and the reasoning system lacks natural language understanding ability

It is this problem that plagues the construction of a large-scale knowledge base system, and also makes it difficult for a knowledge base system built with huge manpower and material resources to provide knowledge services of a certain quality to the public.

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