research-article

Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques

Authors:

Agustín Borrego,

Inma Hernández,

Carlos R. Rivero,

David RuizAuthors Info & Claims

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

Pages 115 - 122

https://doi.org/10.1145/3360901.3364418

Published: 23 September 2019 Publication History

Abstract

Knowledge Graphs (KGs) contain large amounts of structured information. Due to their inherent incompleteness, a process known as KG completion is often carried out to find the missing triples in a KG, usually by training a fact checking model that is able to discern between correct and incorrect knowledge. After the fact checking model has been trained and evaluated, it has to be applied to a set of candidate triples, and those that are considered correct are added to the KG as new knowledge. However, this process needs a set of candidate triples of a reasonable size that represents possible new knowledge, in order to be evaluated by the fact checking task and, if considered to be correct, added to the KG, enriching it. Current approaches for selecting candidate triples for their correctness checking either use the full set possible missing candidate triples (and thus provide no filtering) or apply very basic rules to filter out unlikely candidates, which may have a negative effect on the completion performance as very few candidate triples are filtered out. In this paper we present CHAI, a method for producing more complex rules that are able to filter candidate triples by combining a set of criteria to optimize a fitness function. Our experiments show that CHAI is able to generate rules that, when applied, yield smaller candidate sets than similar proposals while still including promising candidate triples.

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Cited By

Rincon-Yanez DMouakher ASenatore S(2024)Enhancing downstream tasks in Knowledge Graphs Embeddings: A Complement Graph-based Approach Applied to Bilateral TradeProcedia Computer Science10.1016/j.procs.2023.10.364225:C(3692-3700)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.364
Bermudo MAyala DHernández IRuiz DToro M(2024)SpaceRL-KG: Searching paths automatically combining embedding-based rewards with Reinforcement Learning in Knowledge GraphsExpert Systems with Applications10.1016/j.eswa.2024.124410255(124410)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124410
Borrego ADessi DHernandez IOsborne FReforgiato Recupero DRuiz DBuscaldi DMotta E(2022)Completing Scientific Facts in Knowledge Graphs of Research ConceptsIEEE Access10.1109/ACCESS.2022.322024110(125867-125880)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3220241
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Index Terms

Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

September 2019

281 pages

ISBN:9781450370080

DOI:10.1145/3360901

General Chairs:
Mayank Kejriwal
University of Southern California Information Sciences Institute, USA
,
Pedro Szekely
University of Southern California Information Sciences Institute, USA
,
Program Chair:
Raphaël Troncy
EURECOM, France

Copyright © 2019 ACM.

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Published: 23 September 2019

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Ministerio de Ciencia Innovacion y Universidades

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K-CAP '19

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SIGAI

K-CAP '19: Knowledge Capture Conference

November 19 - 21, 2019

CA, Marina Del Rey, USA

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Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Rincon-Yanez DMouakher ASenatore S(2024)Enhancing downstream tasks in Knowledge Graphs Embeddings: A Complement Graph-based Approach Applied to Bilateral TradeProcedia Computer Science10.1016/j.procs.2023.10.364225:C(3692-3700)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.364
Bermudo MAyala DHernández IRuiz DToro M(2024)SpaceRL-KG: Searching paths automatically combining embedding-based rewards with Reinforcement Learning in Knowledge GraphsExpert Systems with Applications10.1016/j.eswa.2024.124410255(124410)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124410
Borrego ADessi DHernandez IOsborne FReforgiato Recupero DRuiz DBuscaldi DMotta E(2022)Completing Scientific Facts in Knowledge Graphs of Research ConceptsIEEE Access10.1109/ACCESS.2022.322024110(125867-125880)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3220241
Omran PTaylor KMendez SHaller A(2022)Active knowledge graph completionInformation Sciences10.1016/j.ins.2022.05.027604(267-279)Online publication date: Aug-2022
https://doi.org/10.1016/j.ins.2022.05.027
Ayala DHernandez IRuiz DRahm E(2021)Towards the smart use of embedding and instance features for property matching2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00209(2111-2116)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00209
Ayala DHernández IRuiz DRahm E(2021)LEAPME: Learning-based Property Matching with EmbeddingsData & Knowledge Engineering10.1016/j.datak.2021.101943(101943)Online publication date: Nov-2021
https://doi.org/10.1016/j.datak.2021.101943

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