A Multi-layer Citation Recommendation Method Based on Document Content Knowledge Graph

Abstract

The invention discloses a multilayer quotation recommendation method based on a literature content mapping knowledge domain, and belongs to the field of information recommendation and intelligent information processing. The method comprises the following steps: firstly, obtaining the query requirement of a user, wherein the query requirement consists of the key words of the title and the digest of a thesis which needs to recommend a quotation thesis or quotation literature; then, on the basis of the literature content mapping knowledge domain, expanding and querying a retrieval word, wherein the mapping knowledge domain consists of the research object word and the research behavior word node of the literature, and edges which express various semantic relations including synonymy, synonym, an up and down position, part-whole, juxtaposition and the like; and finally, constructing the inverted index of the literature in a data set, selecting a candidate quotation, calculating the similarity between the candidate quotation and query, and adopting a gradient progressive regression tree to carry out quotation recommendation. The method carries out multilayer quotation recommendation on the basis of the literature content mapping knowledge domain, enlarges the range of the candidate quotation, accurately expresses the research object and contents of the thesis, improves efficiency for users to obtain a relevant literature and has a wide application prospect.

Description

technical field [0001] The present invention relates to the technical field of information recommendation, in particular to a multi-layer citation recommendation method based on a document content knowledge graph. The invention has broad application prospects in the fields of information recommendation, information retrieval, network public opinion monitoring and the like. Background technique [0002] At present, information recommendation methods can be divided into three categories, content-based recommendation, collaborative filtering-based recommendation, and hybrid methods. [0003] In the content-based recommendation method, the content feature model and user interest model of the recommended object are first constructed, then the similarity between the recommended object and the user's interest is calculated, and finally the recommended object with greater similarity is recommended to the user. Recommended objects and user models usually use keywords to represent fe...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that the recommendation method in the above-mentioned prior art is limited by the number of similar users, it is difficult to retrieve documents with different semantic similarities in characters, it is difficult to retrieve documents with different semantic associations with the research objects and research behaviors of the paper, and The recommended results of cited papers in the prior art cannot well meet the needs of users, and a multi-layered citation recommendation method based on the knowledge graph of document content is provided

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