research-article

An Ontology Enrichment Approach by Using DBpedia

Authors:

Meisam Booshehri,

Peter LukschAuthors Info & Claims

WIMS '15: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics

Article No.: 5, Pages 1 - 11

https://doi.org/10.1145/2797115.2797127

Published: 13 July 2015 Publication History

Abstract

Over the past decade, an increasing number of methods have been proposed for (semi-) automatic generation of ontology from text. However, the ontology generated by these methods usually does not meet the needs of many reasoning-based applications in different domains since most of these methods aim at generating inexpressive ontologies e.g. bare taxonomies. In this paper, a new ontology enrichment approach is proposed in which Web of Linked Data (in particular, DBpedia as one of the huge Linked Data datasets) is used as background knowledge beside text in order to recognize new ontological relations, specifically object properties, for ontology enrichment. In other words, this enrichment approach can be considered as a post-processing step for the "Relations" layer (i.e. the fifth layer) in Ontology Learning Stack, aiming at recommending new object properties to the ontology engineers enabling them to create much more expressive ontologies. This is actually a complementary approach to our recent approach towards adding Linked Data to ontology learning layers where we aimed at improving the functions associated to the "Synonyms" layer, the "Concept Formation" layer and the "Concept Hierarchy" layer of ontology learning stack. In order to evaluate the approach, a customized experimental design is introduced called the "Pseudo Gold Standard based Ontology Evaluation" in which the results obtained by a human expert are compared against those obtained automatically. Finally, the experimental results showed a satisfactory improvement in learning object properties.

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Cited By

Šebek OJirkovský VRychtyckyj NKadera P(2019)Semi-automatic Tool for Ontology Learning TasksIndustrial Applications of Holonic and Multi-Agent Systems10.1007/978-3-030-27878-6_10(119-129)Online publication date: 2-Aug-2019
https://doi.org/10.1007/978-3-030-27878-6_10
Riga MMitzias PKontopoulos EKompatsiaris I(2018)PROPheT – Ontology Population and Semantic Enrichment from Linked Data SourcesData Analytics and Management in Data Intensive Domains10.1007/978-3-319-96553-6_12(157-168)Online publication date: 13-Jul-2018
https://doi.org/10.1007/978-3-319-96553-6_12
Booshehri MLuksch PAnderst-Kotsis G(2016)Towards linked open data enabled ontology learning from textProceedings of the 18th International Conference on Information Integration and Web-based Applications and Services10.1145/3011141.3011184(252-256)Online publication date: 28-Nov-2016
https://dl.acm.org/doi/10.1145/3011141.3011184

Index Terms

An Ontology Enrichment Approach by Using DBpedia
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Information systems
  1. Information retrieval
  2. Information storage systems

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cover image ACM Other conferences

WIMS '15: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics

July 2015

176 pages

ISBN:9781450332934

DOI:10.1145/2797115

Conference Chair:
Rajendra Akerkar
Western Norway Research Institute, Norway
,
General Chair:
Marios Dikaiakos
University of Cyprus, Cyprus
,
Program Chairs:
Achilleas Achilleos
University of Cyprus, Cyprus
,
Tope Omitola
University of Southampton, United Kingdom

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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WNRI: Western Norway Research Institute
University of Cyprus

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2015

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WIMS '15

WIMS '15: 5th International Conference on Web Intelligence, Mining and Semantics

July 13 - 15, 2015

Larnaca, Cyprus

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Cited By

Šebek OJirkovský VRychtyckyj NKadera P(2019)Semi-automatic Tool for Ontology Learning TasksIndustrial Applications of Holonic and Multi-Agent Systems10.1007/978-3-030-27878-6_10(119-129)Online publication date: 2-Aug-2019
https://doi.org/10.1007/978-3-030-27878-6_10
Riga MMitzias PKontopoulos EKompatsiaris I(2018)PROPheT – Ontology Population and Semantic Enrichment from Linked Data SourcesData Analytics and Management in Data Intensive Domains10.1007/978-3-319-96553-6_12(157-168)Online publication date: 13-Jul-2018
https://doi.org/10.1007/978-3-319-96553-6_12
Booshehri MLuksch PAnderst-Kotsis G(2016)Towards linked open data enabled ontology learning from textProceedings of the 18th International Conference on Information Integration and Web-based Applications and Services10.1145/3011141.3011184(252-256)Online publication date: 28-Nov-2016
https://dl.acm.org/doi/10.1145/3011141.3011184

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