Similarity vs. Relevance: From Simple Searches to Complex Discovery

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21240/21:00351284

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Similarity vs. Relevance: From Simple Searches to Complex Discovery

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Similarity vs. Relevance: From Simple Searches to Complex Discovery

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA19-01641S" target="_blank" >GA19-01641S: Kontextové podobnostní vyhledávání v otevřených datech</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-3-030-89656-0

ISSN

—

e-ISSN

—

Počet stran výsledku

14

Strana od-do

104-117

Název nakladatele

Springer

Místo vydání

Cham, Germany

Místo konání akce

virtual

Datum konání akce

29. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—