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Dimensions of Semantic Similarity

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Advances in Data Analysis with Computational Intelligence Methods

Part of the book series: Studies in Computational Intelligence ((SCI,volume 738))

Abstract

Semantic similarity is a broad term used to describe many tools, models and methods applied in knowledge bases, semantic graphs, text disambiguation, ontology matching and more. Because of such broad scope it is, in a “general” case, difficult to properly capture and formalize. So far, many models and algorithms have been proposed that, albeit often very different in design and implementation, produce a single score (a number) each. These scores come under the single term of semantic similarity. Whether one is comparing documents, ontologies, entities, or terms, existing methods often propose a universal score—a single number that “captures all aspects of similarity”. In opposition to this approach, we claim that there are many ways, in which semantic entities can be similar. We propose a division of knowledge (and, consequently, similarity) into categories (dimensions) of semantic relationships. Each dimension represents a different “type” of similarity and its implementation is guided by an interpretation of the meaning (semantics) of that similarity score in a particular dimension. Our proposal allows to add extra information to the similarity score, and to highlight differences and similarities between results of existing methods.

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Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Paweł Szmeja

  2. Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

  3. Warsaw Management Academy, Warsaw, Poland

    Marcin Paprzycki

  4. Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland

    Wiesław Pawłowski

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  1. Paweł Szmeja
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  2. Maria Ganzha
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  3. Marcin Paprzycki
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  4. Wiesław Pawłowski
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Correspondence to Paweł Szmeja .

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Editors and Affiliations

  1. Department of Medicine, Division of Nephrology and Hypertension, University of Louisville, Louisville, Kentucky, USA

    Adam E Gawęda

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Institute of Computational Intelligence, Department of Mechanical Engineering and Computer Science, Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, USA

    Gary G. Yen

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Szmeja, P., Ganzha, M., Paprzycki, M., Pawłowski, W. (2018). Dimensions of Semantic Similarity. In: Gawęda, A., Kacprzyk, J., Rutkowski, L., Yen, G. (eds) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-67946-4_3

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