A method to measure similarity between semantic graphs and its applications

Weijie Liu; Xue Zhou; Shujing Che; Xiang Yu; Zhiran Chen

doi:10.1117/12.3005953

10 October 2023 A method to measure similarity between semantic graphs and its applications

Weijie Liu, Xue Zhou, Shujing Che, Xiang Yu, Zhiran Chen

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127994P (2023) https://doi.org/10.1117/12.3005953
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Semantic search in information retrieval processes plays a crucial role in many application fields, such as product recommendation, personnel search, and those that require information matching. In most semantic search systems, semantic similarity technology is already indispensable. User queries can generate many query graphs. These query graphs can then be used to help users generate search results. Semantic similarity technology focuses on measuring the semantic similarity or semantic distance between two concepts. This paper mainly studies using semantic similarity technology to sort query graphs. To represent the similarity between semantic graphs, we calculate semantic distances. Unlike traditional semantic distance calculation methods, we calculate the distance between graphs. Experiments have confirmed the effectiveness and effectiveness of the method proposed in this paper.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Weijie Liu, Xue Zhou, Shujing Che, Xiang Yu, and Zhiran Chen "A method to measure similarity between semantic graphs and its applications", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127994P (10 October 2023); https://doi.org/10.1117/12.3005953

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