NDLI: Ontology Mapping: As a Binary Classification Problem

Content Provider	IEEE Xplore Digital Library
Author	Ming Mao Yefei Peng Spring, M.
Copyright Year	2008
Abstract	Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. Ontology mapping is critical to achieve semantic interoperability in the WWW. To solve the ontology mapping problem, this paper proposes a non-instance learning-based approach that transforms the ontology mapping problem to a binary classification problem and utilizes machine learning techniques as a solution. Same as other machine learning based approaches, a number of features (i.e., linguistic, structural and web features)are generated for each mapping candidate. However, in contrast to other learning-based mapping approaches, the features proposed in our approach are generic and do not rely on the existence and sufficiency of instances. Therefore our approach can be generalized to different domains without extra training efforts. To evaluate our approach, two experiments (i.e., within-task vs. cross-task) are implemented and the SVM algorithm is applied.Experimental results show that our non-instance learning-based ontology mapping approach performs well on most of OAEI benchmark tests when training and testing on the same mapping task; and the results of approach vary according to the likelihood of training data and testing data when training and testing on different mapping tasks.
Starting Page	20
Ending Page	25
File Size	143367
Page Count	6
File Format	PDF
ISBN	9780769534015
DOI	10.1109/SKG.2008.101
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Performance evaluation Object oriented modeling Training data Machine learning Ontologies Benchmark testing World Wide Web Books Springs
Content Type	Text
Resource Type	Article

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