Book semantic retrieval method based on content structures

Abstract

The invention provides a semantic association retrieval method based on book content structures. In consideration of the difference of contribution degrees of the theme name, catalogue and abstract of a book to the main content of the book and the difference of importance degrees of the chapter titles and section titles of the catalogue in reflecting the content of the book, the book content structures, including the book title, the catalogue structure and the abstract structure, are subjected to Chinese word segmentation and stop word removal processing through a domain dictionary and subjected to synonym expansion through domain ontology to obtain a group of keywords, different weights are given to the keywords in different structures of the book, and the book content with the structure weights is stored through a vector space model. Semantic association expansion is performed on a query word input by a user through the domain ontology, and the similarity between a user retrieval intention vector and a book content structure vector is calculated to more accurately acquire a book most associated with user query content. The method increases the recall ratio and the precision ratio and improves mechanical keyword matching in book retrieval in the prior art.

Description

technical field [0001] The invention relates to the field of digital books, in particular to a method for performing semantic association retrieval on book contents. Background technique [0002] The core competitiveness of a digital library is the accurate retrieval of digital books, and the core of accurate retrieval is an accurate understanding of the contents of books and the user's retrieval intentions. At present, the research on semantic retrieval of book contents lags far behind the actual needs. [0003] At present, users of digital libraries, Dangdang.com, Amazon and other book sales networks mainly search for books based on keyword queries. Users’ query intentions cannot be reasonably understood, and book content information cannot be fully expressed through titles and keywords. Users often need to Through a large number of manual screening to select the target, the setting requires secondary retrieval, which brings great inconvenience to the user, and the recall ...

AI Technical Summary

Problems solved by technology

[0003] At present, users of digital libraries, Dangdang.com, Amazon and other book sales networks mainly search for books based on keyword queries. Users’ query intentions cannot be reasonably understood, and book content information cannot be fully expressed through titles and keywords. Users often need to Through a large number of manual screening to select the target, the setting needs to be searched twice, which brings great inconvenience to the user, and the recall rate and precision rate are not satisfactory.

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