{rfName}
Re

Indexed in

License and use

Citations

1

Altmetrics

Analysis of institutional authors

Mihindukulasooriya NCorresponding AuthorRico MAuthorSantana-Perez IAuthorGarcia-Castro RAuthorGómez-Pérez AAuthor

Share

October 17, 2018
Publications
>
Proceedings Paper
No

Repairing hidden links in linked data: Enhancing the quality of RDF knowledge graphs

Publicated to:Proceedings Of The Knowledge Capture Conference, K-Cap 2017. - 2017-12-04 (), DOI: 10.1145/3148011.3148020

Authors: Mihindukulasooriya, Nandana; Rico, Mariano; Santana-Perez, Idafen; Garcia-Castro, Raul; Gomez-Perez, Asuncion

Affiliations

Univ Politecn Madrid, Ontol Engn Grp - Author
Universidad Politécnica de Madrid - Author

Abstract

© 2017 Copyright held by the owner/author(s). Knowledge Graphs (KG) are becoming core components of most artificial intelligence applications. Linked Data, as a method of publishing KGs, allows applications to traverse within, and even out of, the graph thanks to global dereferenceable identifiers denoting entities, in the form of IRIs. However, as we show in this work, after analyzing several popular datasets (namely DBpedia, LOD Cache, and Web Data Commons JSON-LD data) many entities are being represented using literal strings where IRIs should be used, diminishing the advantages of using Linked Data. To remedy this, we propose an approach for identifying such strings and replacing them with their corresponding entity IRIs. The proposed approach is based on identifying relations between entities based on both ontological axioms as well as data profiling information and converting strings to entity IRIs based on the types of entities linked by each relation. Our approach showed 98% recall and 76% precision in identifying such strings and 97% precision in converting them to their corresponding IRI in the considered KG. Further, we analyzed how the connectivity of the KG is increased when new relevant links are added to the entities as a result of our method. Our experiments on a subset of the Spanish DBpedia data show that it could add 25% more links to the KG and improve the overall connectivity by 17%.

Keywords

Data qualityKnowledge enrichmentKnowledge graphs

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-27:

  • Scopus: 1

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-27:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 28 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MIHINDUKULASOORIYA, NANDANA SAMPATH) and Last Author (GOMEZ PEREZ, ASUNCION DE MARIA).

the author responsible for correspondence tasks has been MIHINDUKULASOORIYA, NANDANA SAMPATH.