STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342409" target="_blank" >RIV/68407700:21230/20:00342409 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3233/FAIA200259" target="_blank" >https://doi.org/10.3233/FAIA200259</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA200259" target="_blank" >10.3233/FAIA200259</a>

Jazyk výsledku

angličtina

Název v původním jazyce

STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment

Popis výsledku v původním jazyce

Relational learning for knowledge base completion has been receiving considerable attention. Intuitively, rule-based strategies are clearly appealing, given their transparency and their ability to capture complex relational dependencies. In practice, however, pure rule-based strategies are currently not competitive with state-of-the-art methods, which is a reflection of the fact that (i) learning high-quality rules is challenging, and (ii) classical entailment is too brittle to cope with the noisy nature of the learned rules and the given knowledge base. In this paper, we introduce STRiKE, a new approach for relational learning in knowledge bases which addresses these concerns. Our contribution is three-fold. First, we introduce a new method for learning stratified rule bases from relational data. Second, to use these rules in a noise-tolerant way, we propose a strategy which extends k-entailment, a recently introduced cautious entailment relation, to stratified rule bases. Finally, we introduce an efficient algorithm for reasoning based on k-entailment.

Název v anglickém jazyce

STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment

Popis výsledku anglicky

Relational learning for knowledge base completion has been receiving considerable attention. Intuitively, rule-based strategies are clearly appealing, given their transparency and their ability to capture complex relational dependencies. In practice, however, pure rule-based strategies are currently not competitive with state-of-the-art methods, which is a reflection of the fact that (i) learning high-quality rules is challenging, and (ii) classical entailment is too brittle to cope with the noisy nature of the learned rules and the given knowledge base. In this paper, we introduce STRiKE, a new approach for relational learning in knowledge bases which addresses these concerns. Our contribution is three-fold. First, we introduce a new method for learning stratified rule bases from relational data. Second, to use these rules in a noise-tolerant way, we propose a strategy which extends k-entailment, a recently introduced cautious entailment relation, to stratified rule bases. Finally, we introduce an efficient algorithm for reasoning based on k-entailment.

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

The proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020)

ISBN

978-1-64368-100-9

ISSN

0922-6389

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1515-1522

Název nakladatele

IOS Press

Místo vydání

Oxford

Místo konání akce

Virtual online

Datum konání akce

29. 8. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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