STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment
Original language description
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.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020)
ISBN
978-1-64368-100-9
ISSN
0922-6389
e-ISSN
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Number of pages
8
Pages from-to
1515-1522
Publisher name
IOS Press
Place of publication
Oxford
Event location
Virtual online
Event date
Aug 29, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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