More Web Proxy on the site http://driver.im/

research-article

ConSim: an enhanced semantic similarity measure to find the relationship between concepts in cross ontology

Authors:

S. K. SrivatsaAuthors Info & Claims

CUBE '12: Proceedings of the CUBE International Information Technology Conference

Pages 303 - 307

https://doi.org/10.1145/2381716.2381773

Published: 03 September 2012 Publication History

Abstract

Ontology has become the key concept for providing relevant and accurate information to the users for a particular domain or from multiple domains. To extract similar concepts from multiple ontologies, there is a need to develop a concept similarity measure between two independent ontologies. The intention of finding semantic similarity is to enhance the integration and retrieval of resources in a more meaningful and accurate way. Based on the existing hybrid similarity measures an enhanced similarity measure is proposed which brings out a more accurate relationship between the two concepts of different ontologies, the proposed measure is equivalent to human judgments. The proposed method has an improved technique over other methods. The experimental results establish the efficiency of our proposed work and reveals that similarity measure for cross ontology out performs the current hybrid similarity measures by 20% in terms of correlation coefficients.

References

[1]

Euripides,G.M. Petrakis, Giannis Varelas, Angelos Hliaoutakis, Paraskevi Raftopoulou, 2006.X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies, Journal of Digital Information Management, Vol. 4, (2006).

[2]

Grosser,M. Luther,T. Muller,J. et al. 1999.Detection of M. tuberculosis DNA in sarcoidosis: correlation with T-cell response. Lab Invest (1999);79:775--84

[3]

http://www.intelligence.tuc.gr/similarity/datasets/CrossOn tologyDataset.pdf

[4]

http://en.wikipedia.org/wiki/WordNet

[5]

Jiang,J.J. and Conrath,D.W.1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proc. ROCLING X, (Taiwan, 1997).

[6]

Leacock, C. and Chodorow, M.1998.Combining local context and WordNet similarity for word sense identification. In WordNet: An Electronic Lexical Database, C. Fellbaum, Ed.Cambridge, MA: MIT Press, 1998, pp. 265--283

[7]

Lin,D.1998.An information-theoretic definition of similarity.In Proc. Int.Conf.Mach.learning, (Jul. 1998), pp. 296--304.

Digital Library

[8]

Nelson,S.J. Johnston,D. and Humphreys,B.L.2001. Relationships in Medical Subject Headings, Relationships in the Organization of Knowledge, pp: 171--184, (2001).

[9]

Nguyen,H.A. and Al-Mubaid,H.2006. New ontology-based semantic similarity measure for the biomedical domain. In Proc. IEEE GrC 2006, pp. 623--628.

[10]

Raymond Y. K. Lau, Dawei Song and Yuefeng Li,2009.Towards A Fuzzy Domain Ontology Extraction Method For Adaptive E-Learning, IEEE Transactions On Knowledge and data Engineering,Vol. 21,No.6,pp.800--813, (June 2009).

Digital Library

[11]

Resnik, P.1995.Using information content to evaluate Semantic similarity.In Proc. 14th Int.Joint Conf. Artif.Intelligence, (Montreal, QC, Canada, 1995),pp. 448--453

Digital Library

[12]

Rodriguez,M.A. and Egenhofer,M.J.2003.Determining semantic similarity among entity classes from different ontologies.IEEE Transactions on Knowledge and Data mining,vol. 15, No. 2, pp: 442--456, (2003)

Digital Library

[13]

Saleena, B. and Srivatsa,S.K.2012.An Automated Approach to Extract Domain Ontology for E-Learning System. Accepted for Publication in International Journal of Innovation and Learning(IJIL),Inderscience Publishers (ISSN: 1741--8089)

[14]

Tversky, A.1997 Features of Similarity. Psychological Review 84(4) (1977) 327--352

[15]

WordNet ontology from http://wordnet.princeton.edu

[16]

Wu,Z. and Palmer, M.1994. Verb Semantics and Lexical Selection. In: Annual Meeting of the Associations for Computational Linguistics (ACL'94), (Las Cruces, New Mexico,1994) 133--138.

Digital Library

Cited By

Nessah DKazar OBenharkat A(2018)Towards a hybrid semantic similarity measure to set the conceptual relatedness in a hierarchyInternational Journal of Metadata, Semantics and Ontologies10.1504/IJMSO.2016.08158311:3(155-164)Online publication date: 17-Dec-2018
https://dl.acm.org/doi/10.1504/IJMSO.2016.081583

Index Terms

ConSim: an enhanced semantic similarity measure to find the relationship between concepts in cross ontology
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Towards the estimation of feature-based semantic similarity using multiple ontologies

A key application of ontologies is the estimation of the semantic similarity between terms. By means of this assessment, the comprehension and management of textual resources can be improved. However, most ontology-based similarity measures only support ...
Enabling semantic similarity estimation across multiple ontologies: An evaluation in the biomedical domain

The estimation of the semantic similarity between terms provides a valuable tool to enable the understanding of textual resources. Many semantic similarity computation paradigms have been proposed both as general-purpose solutions or framed in concrete ...
Semantic similarity estimation from multiple ontologies

The estimation of semantic similarity between words is an important task in many language related applications. In the past, several approaches to assess similarity by evaluating the knowledge modelled in an ontology have been proposed. However, in many ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

CUBE '12: Proceedings of the CUBE International Information Technology Conference

September 2012

879 pages

ISBN:9781450311854

DOI:10.1145/2381716

General Chair:
Vidyasagar Potdar
Curtin University, Australia
,
Program Chair:
Debajyoti Mukhopadhyay
Maharashtra Institute of Technology, India

Copyright © 2012 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]

Sponsors

CUOT: Curtin University of Technology

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 September 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CUBE '12

Sponsor:

CUOT

CUBE '12: CUBE International IT Conference & Exhibition

September 3 - 5, 2012

Pune, India

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
116
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nessah DKazar OBenharkat A(2018)Towards a hybrid semantic similarity measure to set the conceptual relatedness in a hierarchyInternational Journal of Metadata, Semantics and Ontologies10.1504/IJMSO.2016.08158311:3(155-164)Online publication date: 17-Dec-2018
https://dl.acm.org/doi/10.1504/IJMSO.2016.081583

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents