Semantic indexing method based on field ontology

Abstract

The invention relates to a semantic index method based on domain ontology, which comprises steps of (1) using singular value decomposition to decompose term-document matrices to obtain concept term-document matrices, (2) firstly establishing a basic ontology repository of the domain and using the basic ontology repository to construct a dynamic semantic template library based on the domain ontology, (3) establishing an inverted index for the document by using the concept terms which are added to the dynamic semantic template library in step (2) by a semantic index which is a secondary index undertaken on the base of the ontology and relevant statement thereof, and simultaneously establishing secondary indexes for the relevant statements appeared under each ontology. The invention has the advantages that the shortage that the keyword index is difficult to overcome and identify synonyms, semantically same or associated information can be obtained, and the secondary index of the relevant statement of the ontology can refine search granularity, can provide higher accuracy and efficiency for search results, and can provide advanced search functions for users.

Description

technical field [0001] The invention relates to semantic search and semantic network fields, in particular to a semantic index method based on domain ontology. Background technique [0002] With the expansion of network information and the growth of network users, people's use of information and urgent needs are gradually changing, from the previous manual search to the current use of Google, Baidu and other search engines. However, there are still many shortcomings in keyword search, and the accuracy and performance of search are difficult to meet the needs of advanced users. Based on this, we propose a semantic-based indexing technology. It can be seen that the traditional search engines based on keyword matching generally have the following problems. [0003] (1) High recall and low accuracy coexist. Although web pages with highly relevant content have been included in the retrieval results, valuable information is mixed in tens of thousands of other documents with low ...

AI Technical Summary

Problems solved by technology

[0005] (3) Lack of semantic association between query results

If the information required by the user is scattered in many different network documents, the user must construct multiple queries to obtain the required documents, and then manually extract and fuse part of the information in the documents, which increases the burden of the user's corresponding operations and processing.

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