Concept of Relational Similarity Search

D 2022

Concept of Relational Similarity Search

MÍČ, Vladimír and Pavel ZEZULA

Basic information

Original name

Concept of Relational Similarity Search

Authors

MÍČ, Vladimír (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, guarantor, belonging to the institution)

Edition

Cham, Similarity Search and Applications: 15th International Conference, SISAP 2022, Bologna, Italy, October 5 - October 7, 2022, Proceedings, p. 89-103, 15 pp. 2022

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/22:00127336

Organization unit

Faculty of Informatics

ISBN

978-3-031-17848-1

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-17849-8_8

UT WoS

000874756300008

Keywords in English

Efficient similarity search;Relational similarity;Similarity comparisons;Effective similarity search

Abstract

V originále

For decades, the success of the similarity search has been based on a detailed quantification of pairwise similarity of objects. Currently, the search features have become much more precise but also bulkier, and the similarity computations more time-consuming. While the k nearest neighbours (kNN) search dominates the real-life applications, we claim that it is principally free of a need for precise similarity quantifications. Based on the well-known fact that a selection of the most similar alternative out of several options is a much easier task than deciding the absolute similarity scores, we propose the search based on an epistemologically simpler concept of relational similarity. Having arbitrary objects q,o1,o2 from the search domain, the kNN search is solvable just by the ability to choose the more similar object to q out of o1,o2 – the decision can also contain a neutral option. We formalise such searching and discuss its advantages concerning similarity quantifications, namely its efficiency and robustness. We also propose a pioneering implementation of the relational similarity search for the Euclidean spaces and report its extreme filtering power in comparison with 3 contemporary techniques.

Links

EF16_019/0000822, research and development project

Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

MÍČ, Vladimír and Pavel ZEZULA. Concept of Relational Similarity Search. In Tomáš Skopal, Fabrizio Falchi, Jakub Lokoč, Maria Luisa Sapino, Ilaria Bartolini, Marco Patella. Similarity Search and Applications: 15th International Conference, SISAP 2022, Bologna, Italy, October 5 - October 7, 2022, Proceedings. Cham: Springer, 2022, p. 89-103. ISBN 978-3-031-17848-1. Available from: https://dx.doi.org/10.1007/978-3-031-17849-8_8.

@inproceedings{2236342,
   author = {Míč, Vladimír and Zezula, Pavel},
   address = {Cham},
   booktitle = {Similarity Search and Applications: 15th International Conference, SISAP 2022, Bologna, Italy, October 5 - October 7, 2022, Proceedings},
   doi = {http://dx.doi.org/10.1007/978-3-031-17849-8_8},
   editor = {Tomáš Skopal, Fabrizio Falchi, Jakub Lokoč, Maria Luisa Sapino, Ilaria Bartolini, Marco Patella},
   keywords = {Efficient similarity search;Relational similarity;Similarity comparisons;Effective similarity search},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-031-17848-1},
   pages = {89-103},
   publisher = {Springer},
   title = {Concept of Relational Similarity Search},
   url = {https://link.springer.com/chapter/10.1007/978-3-031-17849-8_8},
   year = {2022}
}

TY  - JOUR
ID  - 2236342
AU  - Míč, Vladimír - Zezula, Pavel
PY  - 2022
TI  - Concept of Relational Similarity Search
PB  - Springer
CY  - Cham
SN  - 9783031178481
KW  - Efficient similarity search;Relational similarity;Similarity comparisons;Effective similarity search
UR  - https://link.springer.com/chapter/10.1007/978-3-031-17849-8_8
N2  - For decades, the success of the similarity search has been based on a detailed quantification of pairwise similarity of objects. Currently, the search features have become much more precise but also bulkier, and the similarity computations more time-consuming. While the k nearest neighbours (kNN) search dominates the real-life applications, we claim that it is principally free of a need for precise similarity quantifications. Based on the well-known fact that a selection of the most similar alternative out of several options is a much easier task than deciding the absolute similarity scores, we propose the search based on an epistemologically simpler concept of relational similarity. Having arbitrary objects q,o1,o2 from the search domain, the kNN search is solvable just by the ability to choose the more similar object to q out of o1,o2 – the decision can also contain a neutral option. We formalise such searching and discuss its advantages concerning similarity quantifications, namely its efficiency and robustness. We also propose a pioneering implementation of the relational similarity search for the Euclidean spaces and report its extreme filtering power in comparison with 3 contemporary techniques.
ER  -

MÍČ, Vladimír and Pavel ZEZULA. Concept of Relational Similarity Search. In Tomáš Skopal, Fabrizio Falchi, Jakub Lokoč, Maria Luisa Sapino, Ilaria Bartolini, Marco Patella. \textit{Similarity Search and Applications: 15th International Conference, SISAP 2022, Bologna, Italy, October 5 - October 7, 2022, Proceedings}. Cham: Springer, 2022, p.~89-103. ISBN~978-3-031-17848-1. Available from: https://dx.doi.org/10.1007/978-3-031-17849-8\_{}8.

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