Abstract
Feature-based methods of semantic similarity with Wikipedia achieve fruitful performances on measuring the “likeness” between objects in many research fields. However, since Wikipedia is created and edited by volunteers around the world, the preciseness of these methods more or less are influenced by the incompleteness, invalidity and inconsistency of the knowledge in Wikipedia. Unfortunately, this problem has not got enough attention in the existing work. To address this issue, this paper proposes a novel feature-based method for semantic similarity, which has three parts: low frequency features removal, the similarities of generalized synonyms computing, and weighted feature-based methods based on nonlinear fitting. Moreover, we show that our new method can always get a better Pearson correlation coefficient on one or more benchmarks through a set of experimental evaluations.
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Notes
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- 2.
In the experiments, we have tried different thresholds for low-frequency features between 0 and 2000, and the number 200 works better than others.
- 3.
Hidden categories are used for maintenance of the Wikipedia project which is not part of the encyclopedia. For instance, “1911 Britannica articles needing updates from January 2016” is a hidden category.
References
Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Paşca, M., Soroa, A.: A study on similarity and relatedness using distributional and WordNet-based approaches. In: Proceedings of Human Language Technologies, pp. 19–27. Association for Computational Linguistics (2009)
Camacho-Collados, J., Pilehvar, M.T., Navigli, R.: Nasari: integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities. Artif. Intell. 240, 36–64 (2016)
Choumane, A.: A semantic similarity-based social information retrieval model. Soc. Netw. Anal. Min. 4(1), 1–6 (2014)
Hill, F., Reichart, R., Korhonen, A.: SimLex-999: evaluating semantic models with (genuine) similarity estimation. Comput. Linguist. 41(4), 665–695 (2015)
Jiang, Y., Bai, W., Zhang, X., Hu, J.: Wikipedia-based information content and semantic similarity computation. Inf. Process. Manage. 53(1), 248–265 (2017)
Jiang, Y., Zhang, X., Tang, Y., Nie, R.: Feature-based approaches to semantic similarity assessment of concepts using Wikipedia. Inf. Process. Manage. 51(3), 215–234 (2015)
Meymandpour, R., Davis, J.G.: A semantic similarity measure for linked data: an information content-based approach. Knowl.-Based Syst. 109, 276–293 (2016)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. Comput. Sci. 1–12 (2013)
Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Lang. Cogn. Process. 6(1), 1–28 (1991)
Petrakis, E.G.M., Varelas, G., Hliaoutakis, A., Raftopoulou, P.: X-similarity: computing semantic similarity between concepts from different ontologies. J. Digit. Inf. Manage. 4(4), 233–237 (2006)
Qu, R., Fang, Y., Bai, W., Jiang, Y.: Computing semantic similarity based on novel models of semanticrepresentation using Wikipedia. Inf. Process. Manage. 54, 1002–1021 (2018)
Rodríguez, A., Egenhofer, M.J.: Determining semantic similarity among entity classes from different ontologies. IEEE Trans. Knowl. Data Eng. 15(2), 442–456 (2003)
Rubenstein, H., Goodenough, J.B.: Contextual correlates of synonymy. Commun. Assoc. Comput. Mach. 8(10), 627–633 (1965)
Zhu, G., Iglesias, C.A.: Computing semantic similarity of concepts in knowledge graphs. IEEE Trans. Knowl. Data Eng. 29(1), 72–85 (2017)
Acknowledgments
The works described in this paper are supported by The National Natural Science Foundation of China under Grant Nos. 61772210 and 61272066; Guangdong Province Universities Pearl River Scholar Funded Scheme (2018); The Project of Science and Technology in Guangzhou in China under Grant No. 201807010043; The key project in universities in Guangdong Province of China under Grant No. 2016KZDXM024.
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Huang, G., Jiang, Y., Ma, W., Liu, W. (2019). Assessing Semantic Similarity Between Concepts Using Wikipedia Based on Nonlinear Fitting. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_16
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