Literature entity relationship discovery method and system based on knowledge graph

Abstract

The invention discloses a literature entity relationship discovery method and system based on a knowledge graph. The method comprises the following steps of constructing the knowledge graph accordingto the entities in the literature contents and the relationships among the entities; extracting the RDF data in the knowledge graph, and performing vectorization processing on the RDF data to obtain the vector data; obtaining a native entity relationship vector group and a non-connected entity relationship vector group according to the association relationship of the vector data; carrying out thevector matching degree calculation on the unconnected entity relationship vector groups, and screening out the unconnected entity relationship vector groups with the vector matching degrees greater than a preset threshold value, or sorting the unconnected entity relationship vector groups according to the vector matching degrees obtained through calculation. According to the method, the knowledgegraph is constructed by taking the entity in the literature content as the granularity; and the entity relationship matching is carried out through the vector calculation based on the knowledge graph,and the potential entity relationships among literature contents can be deeply mined, so that an innovative research method is provided to find the potential knowledge which is not found by human beings, and the development process of human science is accelerated.

Description

technical field [0001] The invention relates to the technical fields of information science and knowledge engineering, in particular to a method for discovering a document entity relationship based on a knowledge map and a system for applying the method. Background technique [0002] In the vast ocean in the academic field, there are many potential knowledge relationships or knowledge contents buried in the literature that have not yet been tapped. This is also the content of the work that scholars search for inspiration through a large number of literature reviews. This is the process of knowledge discovery. [0003] Document-based knowledge discovery, a more precise term is "knowledge discovery based on non-related documents", which was first proposed by Professor Swanson in 1986, and its meaning refers to: "Discovering some knowledge fragments from published non-related documents The implicit connection between them, and on this basis, scientific hypotheses or conjectures...

AI Technical Summary

Problems solved by technology

On the one hand, the content retrieved or recommended by the ArrowSmith system is mainly based on the granularity of article titles or abstracts, and the recommendation requires the user to input search index keywords for judgment, which belongs to local calculation and has low accuracy; on the other hand, ArrowSmith The system needs to artificially assume preset associations. Each assumed association takes a lot of time and energy in the literature link, and it may not be found, and the efficiency is low.

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