dc.creator | Borrego Díaz, Agustín | es |
dc.creator | Ayala Hernández, Daniel | es |
dc.creator | Hernández Salmerón, Inmaculada Concepción | es |
dc.creator | Rivero, Carlos R. | es |
dc.creator | Ruiz Cortés, David | es |
dc.date.accessioned | 2021-02-17T10:29:05Z | |
dc.date.available | 2021-02-17T10:29:05Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Borrego Díaz, A., Ayala Hernández, D., Hernández Salmerón, I.C., Rivero, C.R. y Ruiz Cortés, D. (2019). Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques. En K-CAP 2019: 10th International Conference on Knowledge Capture (115-122), Marina del Rey, CA, USA: ACM Digital Library. | |
dc.identifier.isbn | 978-1-4503-7008-0 | es |
dc.identifier.uri | https://hdl.handle.net/11441/105070 | |
dc.description.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. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2016-75394-R | es |
dc.format | application/pdf | es |
dc.format.extent | 8 | es |
dc.language.iso | eng | es |
dc.publisher | ACM Digital Library | es |
dc.relation.ispartof | K-CAP 2019: 10th International Conference on Knowledge Capture (2019), pp. 115-122. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Knowledge Graphs | es |
dc.subject | Knowledge Graph Completion | es |
dc.subject | Candidate filtering | es |
dc.title | Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2016-75394-R | es |
dc.relation.publisherversion | https://dl.acm.org/doi/10.1145/3360901.3364418 | es |
dc.identifier.doi | 10.1145/3360901.3364418 | es |
dc.publication.initialPage | 115 | es |
dc.publication.endPage | 122 | es |
dc.eventtitle | K-CAP 2019: 10th International Conference on Knowledge Capture | es |
dc.eventinstitution | Marina del Rey, CA, USA | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |