Mid-Ontology learning from linked data

Authors:
Lihua Zhao;Ryutaro Ichise
Affiliations:
Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan;Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan
Venue:
JIST'11 Proceedings of the 2011 joint international conference on The Semantic Web
Year:
2011

Citing 11
Cited 1

An algorithm for suffix stripping

Readings in information retrieval
A survey on ontology mapping

ACM SIGMOD Record
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications

Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Matching

Ontology Matching
Ontology learning: state of the art and open issues

Information Technology and Management
WordNet: similarity - measuring the relatedness of concepts

AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SameAs networks and beyond: analyzing deployment status and implications of owl:sameAs in linked data

ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
When owl: sameAs isn't the same: an analysis of identity in linked data

ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Linking and building ontologies of linked data

ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Linked Data

Linked Data
Creating knowledge out of interlinked data

Semantic Web

Graph-based ontology analysis in the linked open data

Proceedings of the 8th International Conference on Semantic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Linking Open Data(LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 26 billion RDF triples. Consuming linked data is a challenging task because each data set in the LOD cloud has specific ontology schema, and familiarity with ontology schema is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experimental results show that our Mid-Ontology learning approach successfully integrates diverse ontology schema, and effectively retrieves related information.