JRM Vol.30 No.6 pp. 958-964
doi: 10.20965/jrm.2018.p0958


Improved Synthetic Weighted Algorithm of Ontology-Based Semantic Similarity Computation

Yuan Liu*, Haiquan Wang**, and Xiguang Zhang**

*School of Mechatronics Engineering, Zhengzhou University of Aeronautics
No.15 Wenyuan Road, Zhengzhou 450015, China

**Zhongyuan Petersburg Aviation College, Zhongyuan University of Technology
No.41 Zhongyuan Road, Zhengzhou 450007, China

May 8, 2018
October 17, 2018
December 20, 2018
semantic similarity, path coincidence degree, shortest distance, degree of concept, LCA density

To solve the problems of incomplete consideration and low precision in existing domain ontology semantic similarity computation, an improved synthetic weighted algorithm of ontology-based semantic similarity computation is proposed, mixing path coincidence degree, the shortest distance, and concept property methods. First, the depths of lowest common ancestor (LCA) and an ontology tree are added to the formula of path coincidence degree for distinguishing the influence of LCA depth on similarity when multiple inheritances occur. Second, the analysis of similarity algorithm based on the shortest distance cannot distinguish two situations with the same path distance. One is when the density of LCA is different. The other is a depth difference in the concept pair. So, the number of direct subnodes of the LCA and the depth difference are added to the formula of the shortest distance. Meanwhile, the switch of density factor is set to ensure similarity calculation results between [0,1]. Then, a synthetic weighted algorithm of semantic similarity computation is constructed using the weighting path coincidence degree, the shortest distance, and the concept property. Finally, this algorithm and the other three algorithms in the literature are used to calculate semantic similarity in tea ontology. The results show that this algorithm is closest to expert experience.

Similarity tendency of different methods

Similarity tendency of different methods

Cite this article as:
Y. Liu, H. Wang, and X. Zhang, “Improved Synthetic Weighted Algorithm of Ontology-Based Semantic Similarity Computation,” J. Robot. Mechatron., Vol.30 No.6, pp. 958-964, 2018.
Data files:
  1. [1] M. Zhou, Y. Ding, and C. Huang, “Improving translation selection with a new translation model trained by independent monolingual corpora,” Computational Linguistics and Chinese Language Processing, Vol.6, No.1, pp. 1-26, 2001.
  2. [2] W. Lu, H. Huang, and H. Wu, “Word sense disambiguation wit graph model based on domain knowledge,” Acta Automatica Sinica, Vol.40, No.12, pp. 2836-2850, 2014.
  3. [3] H. Hassan, A. Hassan, and O. Emam, “Unsupervised information extraction approach using graph mutual reinforcement,” Proc. of the 2006 Conf. on Empirical Methods in Natural Language Processing, Stroudsburg PAUSA Association for Computational Linguistics, pp. 501-508, 2006.
  4. [4] L. Liao, G. Shen, and Z. Huang, “An approach for ontology cohesion metrics based on directed acyclic graph,” Computer Engineering & Science, Vol.37, No.7, pp. 1297-1303, 2015.
  5. [5] P. Resnik, “Using information content to evaluate semantic similarity in a taxonomy,” Proc. of the 14th Int. Joint Conf. on Artificial Intelligence, pp. 448-453, 1995.
  6. [6] D. Lin, “An information-theoretic definition of similarity,” Proc. of the 15th Int. Conf. on Machine Learning, pp. 296-304, 1998.
  7. [7] Z. Wu and M. Palmer, “Verbs semantics and lexical selection,” Proc. of the 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133-138, 1994.
  8. [8] C. Leacock and M. Chodorow, “Combining Local Context and WordNet Similarity for Word Sense Identification,” WordNet: An Electronic Lexical Database, pp. 265-283, 1998.
  9. [9] A. Tversky, “Features of similarity,” Psycological Review, Vol.84, No.4, pp. 327-352, 1977.
  10. [10] H. Zhang, W. Xing, and Y. Cai, “A WordNet-based hybrid semantic similarity measurement,” Computer Engineering & Science, Vol.39, No.5, pp. 971-976, 2017.
  11. [11] B. Ding and S. Miao, “Research on concept semantic similarity of manufacturing resource ontology,” Application Research of Computers, Vol.33, No.1, pp. 28-32, 2016.
  12. [12] Z. Zheng, C. Ruan, and L. Li, “Adaptive ontology semantic similarity comprehensive weighted algorithm,” Computer Science, Vol.43, No.10, pp. 242-247, 2016.
  13. [13] X. Han, Q. Wang, and Y. Guo, “Geographic ontology concept semantic similarity measure model based on BP neural network optimized by PSO,” Computer Engineering and Application, Vol.53, No.8, pp. 32-37, 2017.
  14. [14] J. Suo and Y. Liu, “Semantic similarity algorithm based on agricultural ontology and its application on crop ontology,” Trans. of the Chinese Society of Agricultural Engineering, Vol.32, No.16, pp. 175-182, 2016.
  15. [15] G. Huang, Z. Zhou, and T. Zhou, “Research on the Domain-Ontology- Based Semantic Similarity Computing,” Computer Engineering and Science, Vol.29, No.5, pp. 112-116, 2007.
  16. [16] W. Li, X. Sun, and C. Zhang, “A semantic similarity measure between ontological concepts,” Acta Automatica Sinica, Vol.38, No.2, pp. 229-235, 2012.
  17. [17] B. You, Y. Yan, and Y. Sun, “Method of information content evaluating semantic similarity on HowNet,” Computer System & Applications, Vol.22, No.1, pp. 129-133, 2013.
  18. [18] X. Chen, “A concept similarity computation based-on muliproperty ontology,” Northeast Normal University, 2010.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024