Dimensions of semantic similarity

Paweł Szmeja , Maria Ganzha , Marcin Paprzycki , Wiesław Pawłowski


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, pro- duce 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.
Author Paweł Szmeja
Paweł Szmeja,,
, Maria Ganzha
Maria Ganzha,,
, Marcin Paprzycki
Marcin Paprzycki,,
, Wiesław Pawłowski (FMPI / II)
Wiesław Pawłowski,,
- Institute of Informatics
Publication size in sheets3.2
Book Gawęda Adam E., Kacprzyk Janusz, Rutkowski Leszek, Yen Gary G. (eds.): Advances in data analysis with computational intelligence methods: dedicated to Professor Jacek Żurada, Studies in Computational Intelligence, no. 738, 2018, Springer, ISBN 978-3-319-67945-7, [978-3-319-67946-4 ], 432 p., DOI:10.1007/978-3-319-67946-4
ASJC Classification1702 Artificial Intelligence
URL https://link.springer.com/content/pdf/10.1007%2F978-3-319-67946-4.pdf
Languageen angielski
Score (nominal)20
Score sourcepublisherList
ScoreMinisterial score = 20.0, 28-01-2020, MonographChapterAuthor
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.447
Citation count*1 (2020-03-24)
Share Share

Get link to the record

* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
Are you sure?