Open Access

ARTICLE

Improved Algorithm Based on Decision Tree for Semantic Information Retrieval

Zhe Wang^1,2, Yingying Zhao¹, Hai Dong³, Yulong Xu^1,*, Yali Lv¹

1 School of Information Technology, Henan University of Chinese Medicine, Zhengzhou, 450046, China
2 School of Information Science and Technology, Donghua University, Shanghai, 201620, China
3 School of Computing Technologies, RMIT University, Melbourne, VIC 3001, Australia

* Corresponding Author: Yulong Xu. Email:

Intelligent Automation & Soft Computing 2021, 30(2), 419-429. https://doi.org/10.32604/iasc.2021.016434

Received 02 January 2021; Accepted 30 April 2021; Issue published 11 August 2021

Abstract

The quick retrieval of target information from a massive amount of information has become a core research area in the field of information retrieval. Semantic information retrieval provides effective methods based on semantic comprehension, whose traditional models focus on multiple rounds of detection to differentiate information. Since a large amount of information must be excluded, retrieval efficiency is low. One of the most common methods used in classification, the decision tree algorithm, first selects attributes with higher information entropy to construct a decision tree. However, the tree only matches words on the grammatical level and does not consider the semantic of the information and lacks understanding of the information; meanwhile, it increases the amount of calculation and the complexity of the algorithm on synonymous fields, and the classification quality is not high. We investigate the retrieval method, unstructured processing with different semantic data, extracting the attribute features of semantic information, creating a multi-layered structure for the attribute features, calculating the window function according to the theory of multi-level analytic fusion, and fusing different levels of data. Then, we calculate the expected entropy of semantic information, undertake the boundary treatment of the attributes, calculate the information gain and information gain ratio of the attributes, and set the largest gain ratio of semantic data as the nodes of the decision tree. Our results reveal the algorithm’s superior effectiveness in semantic information retrieval. Experimental results verify that the algorithm improves the expressing ability of knowledge in the information retrieval system and improves the time efficiency of semantic information retrieval.

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