Document semantic similarity calculation method

Abstract

The invention provides a document semantic similarity calculation method. According to the method, the document retrieval workload is reduced, the working efficiency is improved, a threshold value setting method is adopted, and the similarity is calculated by intervals. The method specifically comprises steps as follows: A, constructing one or more sets of body libraries: each body library is formed by inputting of concept systems and main description terms, and in each body library, concepts form concept trees according to correlations and the concept trees form concept forests; B, calculating the semantic similarity: the semantic similarity between a query object vQuerym and a document vDocm is calculated with a tf-idf (term frequency-inverse document frequency) algorithm, the calculation formula is shown in the specification, tf is the number of times of appearance of the query object in the document, idf is the metric of the general importance of the query object, sim(cmi and cnj) is the semantic similarity between cmi and cnj, the calculation formula is shown in the specification, d is the distance between every two concepts in each concept tree, c is an automatic regulation parameter along with the system, p is the predefined correlation between cmi and cnj in each body, and the default is 1.

Description

technical field [0001] The invention relates to the technical field of document-oriented intelligent information retrieval, in particular to a method for calculating document semantic similarity. Background technique [0002] Semantic computing is a kind of writing information content based on the meaning and vocabulary shared by users and computers. On the basis of people's real life, thus enriching the meaning and value of the whole real world. [0003] A search engine based on keyword matching judges whether a query matches a document through literal matching of keywords, which is a binary logic; while a semantic search engine theoretically has a non-identical relationship between most documents and the concept being queried. From the definition of semantic similarity, it can be found that the existence of this phenomenon is due to the non-zero similarity between most words in the word similarity matrix. This will pose a huge challenge to the storage capacity of the sys...

AI Technical Summary

Problems solved by technology

This will pose a huge challenge to the storage capacity of the system, which is also an important reason why some semantic computing methods cannot be applied in large-scale systems

[0004] Therefore, the smaller similarity value in the similarity matrix is ​​not meaningful, but also brings a huge burden to the system, and the data in the similar matrix needs to be screened

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