The Method for Semantic Similarity Based on Concept Distance

DOI

10.2991/msbda-19.2019.37How to use a DOI?

Keywords

Semantic web of things, Semantic matching, Service discovery, Semantic concept similarity

Abstract

Semantic matching is an important problem of service discovery. In order to find effective services, a method for semantic concept similarity is proposed. The method calculates the concept similarity between the parameter concepts of services by directly using the distance relationship between the concept nodes in the classification tree. And uses the nonlinear function to calculate the similarity and redefine the concept-based similarity between concepts. The new method effectively solves problems in existing algorithms and further improves precision. Finally, theoretical analysis and experimental result reveals the validity of the proposed method.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xinying Chen
AU  - Guanyu Li
AU  - Heng Chen
AU  - Yunhao Sun
AU  - Wei Jiang
PY  - 2019/08
DA  - 2019/08
TI  - The Method for Semantic Similarity Based on Concept Distance
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
PB  - Atlantis Press
SP  - 243
EP  - 248
SN  - 2352-538X
UR  - https://doi.org/10.2991/msbda-19.2019.37
DO  - 10.2991/msbda-19.2019.37
ID  - Chen2019/08
ER  -