Approaches to measure class importance in Knowledge Graphs.

The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the sa...

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Autores principales: Daniel Fernández-Álvarez, Johannes Frey, Jose Emilio Labra Gayo, Daniel Gayo-Avello, Sebastian Hellmann
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/6ecfebc70ede4313a00789fdb771b10e
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spelling oai:doaj.org-article:6ecfebc70ede4313a00789fdb771b10e2021-12-02T20:10:49ZApproaches to measure class importance in Knowledge Graphs.1932-620310.1371/journal.pone.0252862https://doaj.org/article/6ecfebc70ede4313a00789fdb771b10e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252862https://doaj.org/toc/1932-6203The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea which groups many individuals (or instances) in the context of a given KG, which is handy to use when producing summaries of its content. Rankings of class importance are a powerful summarization tool that can be used both to obtain a superficial view of the content of a given KG and to prioritize many different actions over the data (data quality checking, visualization, relevance for search engines…). In this paper, we analyze existing techniques to measure class importance and propose a novel approach called ClassRank. We compare the class usage in SPARQL logs of different KGs with the importance ranking produced by the approaches evaluated. Then, we discuss the strengths and weaknesses of the evaluated techniques. Our experimentation suggests that ClassRank outperforms state-of-the-art approaches measuring class importance.Daniel Fernández-ÁlvarezJohannes FreyJose Emilio Labra GayoDaniel Gayo-AvelloSebastian HellmannPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252862 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Daniel Fernández-Álvarez
Johannes Frey
Jose Emilio Labra Gayo
Daniel Gayo-Avello
Sebastian Hellmann
Approaches to measure class importance in Knowledge Graphs.
description The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea which groups many individuals (or instances) in the context of a given KG, which is handy to use when producing summaries of its content. Rankings of class importance are a powerful summarization tool that can be used both to obtain a superficial view of the content of a given KG and to prioritize many different actions over the data (data quality checking, visualization, relevance for search engines…). In this paper, we analyze existing techniques to measure class importance and propose a novel approach called ClassRank. We compare the class usage in SPARQL logs of different KGs with the importance ranking produced by the approaches evaluated. Then, we discuss the strengths and weaknesses of the evaluated techniques. Our experimentation suggests that ClassRank outperforms state-of-the-art approaches measuring class importance.
format article
author Daniel Fernández-Álvarez
Johannes Frey
Jose Emilio Labra Gayo
Daniel Gayo-Avello
Sebastian Hellmann
author_facet Daniel Fernández-Álvarez
Johannes Frey
Jose Emilio Labra Gayo
Daniel Gayo-Avello
Sebastian Hellmann
author_sort Daniel Fernández-Álvarez
title Approaches to measure class importance in Knowledge Graphs.
title_short Approaches to measure class importance in Knowledge Graphs.
title_full Approaches to measure class importance in Knowledge Graphs.
title_fullStr Approaches to measure class importance in Knowledge Graphs.
title_full_unstemmed Approaches to measure class importance in Knowledge Graphs.
title_sort approaches to measure class importance in knowledge graphs.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/6ecfebc70ede4313a00789fdb771b10e
work_keys_str_mv AT danielfernandezalvarez approachestomeasureclassimportanceinknowledgegraphs
AT johannesfrey approachestomeasureclassimportanceinknowledgegraphs
AT joseemiliolabragayo approachestomeasureclassimportanceinknowledgegraphs
AT danielgayoavello approachestomeasureclassimportanceinknowledgegraphs
AT sebastianhellmann approachestomeasureclassimportanceinknowledgegraphs
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