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Knowledge graph-based semantic similarity calculation method for financial industry software testing

A technology of semantic similarity and knowledge graph, applied in the field of semantic similarity calculation based on knowledge graph for software testing in the financial industry, can solve the problems of complex grammatical structure and few applications, and achieve the effect of good performance

Pending Publication Date: 2019-09-13
SOUTH CHINA UNIV OF TECH +1
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AI Technical Summary

Problems solved by technology

However, most of the applications of knowledge graphs for semantic similarity detection are mainly English texts, and there are fewer applications in Chinese.
Financial texts are different from English texts, there are no spaces between words, and the grammatical structure of Chinese is relatively more complicated

Method used

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  • Knowledge graph-based semantic similarity calculation method for financial industry software testing
  • Knowledge graph-based semantic similarity calculation method for financial industry software testing
  • Knowledge graph-based semantic similarity calculation method for financial industry software testing

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Experimental program
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Embodiment

[0055] like image 3 As shown, the financial industry-oriented software testing method of the present invention is based on the knowledge map semantic similarity calculation method, comprising the following steps:

[0056] S1. Perform word segmentation operations on financial texts;

[0057] S2. Selecting the most relevant to the topic of the text to obtain different word segmentation combinations;

[0058] S3. Using the knowledge graph and the concept IC weighted minimum path length to calculate the semantic similarity of word segmentation combinations.

[0059] Further, step S1 includes:

[0060] Segment Chinese sentences through a variety of word segmentation algorithms to obtain different word segmentation combinations;

[0061] 1. Jieba

[0062] 1) Through the dict.txt dictionary that comes with Jieba word segmentation, according to the trie tree generated by the dict.txt dictionary, generate all possible word formations in the sentence to form a DAG directed acyclic ...

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Abstract

The invention provides a knowledge graph-based semantic similarity calculation method for financial industry software testing. The method comprises the following steps: S1, carrying out word segmentation operation on a financial text; s2, selecting a word segmentation combination most related to the text subject; and S3, calculating the semantic similarity of the segmented word combination by using the knowledge graph and using the concept IC weighted minimum path length. The natural semantic detection algorithm based on the knowledge graph comprises the following steps: firstly, carrying outword segmentation on a financial text by utilizing multiple word segmentation algorithms to obtain a word segmentation combination; calculating the concept distance between the word and the text keyword to measure the similarity between the word segmentation combination and the text topic; and finally, selecting the word segmentation combination with the minimum concept distance sum to carry out semantic similarity detection. The information IC of the concept is used in the knowledge graph to weigh the shortest path length between the concepts, and better performance is shown in accuracy compared with other methods.

Description

technical field [0001] The invention belongs to the field of natural language processing, in particular to a method for computing semantic similarity based on knowledge graphs for software testing in the financial industry. Background technique [0002] Semantic similarity detection between natural languages ​​is widely used in many fields such as information retrieval, machine translation, and automatic question answering. The sentence itself is composed of words, including subjects, predicates, and various stop words. Even the same words, in different combinations and in different situations, will have completely different meanings. In recent years, many calculation methods based on statistics have been proposed, but this method ignores the semantic information and sentence structure information of the text, and the calculation results sometimes do not conform to people's understanding of natural language, and the current corpus-based IC is due to IC is calculated by cou...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G06F17/22
CPCG06F40/194G06F40/279G06F40/30
Inventor 杜广龙陈震星李方周文沛孙慧姚庚成
Owner SOUTH CHINA UNIV OF TECH
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