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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Daniel Fernández-Álvarez, Johannes Frey, Jose Emilio Labra Gayo, Daniel Gayo-Avello, Sebastian Hellmann
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6ecfebc70ede4313a00789fdb771b10e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6ecfebc70ede4313a00789fdb771b10e
record_format	dspace
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
_version_	1718374952292319232

Approaches to measure class importance in Knowledge Graphs.

Ejemplares similares