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Density self-adaption conceptual semantic similarity calculation method based on ontology

A technology for calculating semantic similarity and similarity. It is applied in computing, semantic analysis, natural language data processing, etc. to achieve the effect of good versatility, easy promotion and strong practicability.

Inactive Publication Date: 2018-07-06
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that the introduction of regional density through a smoothing parameter in the edge-based method is a method based on empirical values, and combine the edge-based method with information theory to propose an ontology-based density adaptive Conceptual semantic similarity calculation method, so that it does not require additional parameters and can adapt to the influence of different densities on edges

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  • Density self-adaption conceptual semantic similarity calculation method based on ontology
  • Density self-adaption conceptual semantic similarity calculation method based on ontology
  • Density self-adaption conceptual semantic similarity calculation method based on ontology

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Embodiment 1

[0058] figure 1 It is a flow chart of an ontology-based density adaptive concept semantic similarity calculation method of the present invention, figure 2 It is a structural schematic diagram of an ontology-based density-adaptive concept semantic similarity calculation method of the present invention. As can be seen from the figure, the method includes the following steps:

[0059] Step A: Input the concepts bird and cock, query the ontology WordNet3.0, and get the meanings corresponding to bird and cock respectively;

[0060] The meanings of the concept bird are: {bird}, {bird, fowl}, {dame, doll, wench, skirt, chick, bird}, {boo, hoot, Bronx_cheer, hiss, raspberry, razzing, razz, snort, bird}, {shuttlecock, bird, birdie, shuttle};

[0061] The meanings of the concept cock are: {cock, prick, dick, shaft, pecker, peter, tool, putz}, {stopcock, cock, turncock}, {hammer, cock}, {cock, rooster}, {cock};

[0062] Among them, in WordNet3.0, each node is represented by a set, a...

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Abstract

The invention relates to a density self-adaption conceptual semantic similarity calculation method based on ontology, and belongs to the technical field of natural language understanding in the artificial intelligence field. The semantic similarity calculation method includes the steps that an edge weight function is derived by combining an edge statistic model and information theories, then the depths of concepts and shortest path weights between the concepts are expressed, finally the depths and the shortest path weights are applied to a traditional method based on edges, and the conceptualsemantic similarity is calculated. The model has the performance similar to that of an existing method which has the best effect and is based on information content, additional parameters are not needed, the model can self-adapt to the influences of different densities on the edges, good universality is achieved, time complexity has obvious advantages compared with the method based on the information content, and the conceptual semantic similarity method is high in performance and efficiency, and has better application prospects.

Description

technical field [0001] The invention relates to a method for calculating conceptual semantic similarity, in particular to an ontology-based density adaptive conceptual semantic similarity calculation method, which belongs to the technical field of natural language understanding in the field of artificial intelligence. Background technique [0002] The calculation of conceptual semantic similarity is a basic research content of natural language processing, and it has a wide range of applications in the fields of intelligent retrieval, word sense disambiguation, machine learning, spelling correction, machine translation, and information extraction. At present, the research strategies of conceptual semantic similarity calculation can be roughly divided into three categories: one is to use large-scale corpus for statistics, which mainly uses the probability distribution of contextual information as the reference basis for conceptual semantic similarity, which requires a coverage ...

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/3344G06F16/367G06F40/247G06F40/30
Inventor 李飞廖乐健何景
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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