Inferring types on large datasets applying ontology class hierarchy classifiers

Rico-Almodóvar, Mariano and Santana Pérez, Idafen and Pozo Jiménez, Pedro del and Gómez-Pérez, A. (2018). Inferring types on large datasets applying ontology class hierarchy classifiers. In: "EKAW 2018", 12-16 Nov 2018, Nancy, Francia. ISBN 978-3-030-03666-9. pp. 322-337. https://doi.org/10.1007/978-3-030-03667-6_21.

Description

Title: Inferring types on large datasets applying ontology class hierarchy classifiers
Author/s:
  • Rico-Almodóvar, Mariano
  • Santana Pérez, Idafen
  • Pozo Jiménez, Pedro del
  • Gómez-Pérez, A.
Item Type: Presentation at Congress or Conference (Article)
Event Title: EKAW 2018
Event Dates: 12-16 Nov 2018
Event Location: Nancy, Francia
Title of Book: Knowledge Engineering and Knowledge Management
Date: 2018
ISBN: 978-3-030-03666-9
Volume: 1
Subjects:
Freetext Keywords: DBpedia, Machine learning, Type prediction, Data quality, Linked data, Semantic web
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Adding type information to resources belonging to large knowledge graphs is a challenging task, specially when considering those that are generated collaboratively, such as DBpedia, which usually contain errors and noise produced during the transformation process from different data sources. It is important to assign the correct type(s) to resources in order to efficiently exploit the information provided by the dataset. In this work we explore how machine learning classification models can be applied to solve this issue, relying on the information defined by the ontology class hierarchy. We have applied our approaches to DBpedia and compared to the state of the art, using a per-level analysis. We also define metrics to measure the quality of the results. Our results show that this approach is able to assign 56% more new types with highe precision and recall than the current DBpedia state of the art.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2016-78011-C4-4-RUnspecifiedUnspecifiedUnspecified
Government of SpainRTC- 2016-4952-7UnspecifiedUnspecifiedUnspecified

More information

Item ID: 72463
DC Identifier: https://oa.upm.es/72463/
OAI Identifier: oai:oa.upm.es:72463
DOI: 10.1007/978-3-030-03667-6_21
Official URL: https://link.springer.com/chapter/10.1007/978-3-030-03667-6_21
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 25 Jan 2023 11:24
Last Modified: 25 Jan 2023 11:24
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