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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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