Similarity vs. Relevance: From Simple Searches to Complex Discovery

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10433259" target="_blank" >RIV/00216208:11320/21:10433259 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/21:00351284

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_9" target="_blank" >http://dx.doi.org/10.1007/978-3-030-89657-7_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_9" target="_blank" >10.1007/978-3-030-89657-7_9</a>

Result language

angličtina

Original language name

Similarity vs. Relevance: From Simple Searches to Complex Discovery

Original language description

Similarity queries play the crucial role in content-based retrieval. The similarity function itself is regarded as the function of relevance between a query object and objects from database; the most similar objects are understood as the most relevant. However, such an automatic adoption of similarity as relevance leads to limited applicability of similarity search in domains like entity discovery, where relevant objects are not supposed to be similar in the traditional meaning. In this paper, we propose the meta-model of data-transitive similarity operating on top of a particular similarity model and a database. This meta-model enables to treat directly non-similar objects x , y as similar if there exists a chain of objects x , i1 , ..., in , y having the neighboring members similar enough. Hence, this approach places the similarity in the role of relevance, where objects do not need to be directly similar but still remain relevant to each other (transitively similar). The data-transitive similarity concept allows to use standard similarity-search methods (queries, joins, rankings, analytics) in more complex tasks, like the entity discovery, where relevant results are often complementary or orthogonal to the query, rather than directly similar. Moreover, we show the data-transitive similarity is inherently self-explainable and non-metric. We discuss the approach in the domain of open dataset discovery.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-01641S" target="_blank" >GA19-01641S: Contextual Similarity Search in Open Data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

14th International Conference, SISAP 2021, Dortmund, Germany, September 29 – October 1, 2021, Proceedings

ISBN

978-3-030-89656-0

ISSN

—

e-ISSN

—

Number of pages

14

Pages from-to

104-117

Publisher name

Springer

Place of publication

Cham, Germany

Event location

virtual

Event date

Sep 29, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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