research-article

Discovering semantic similarity association in semantic search system

Authors:

Shahdad Shariatmadari,

Hamidah Ibahim,

Norwati MustaphaAuthors Info & Claims

iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services

Pages 331 - 337

https://doi.org/10.1145/1806338.1806398

Published: 14 December 2009 Publication History

Abstract

Discovering semantic similarity association among ontology instances is a challenging problem in semantic search systems. In a populated ontology there are numbers of different paths emanating from entities at instance level. Computing semantic similarity between these paths is an important issue in semantic analysis and semantic search applications.

To answer some complex queries about the relatedness of two entities, we need to discover semantic similarity association between entities. Each entity has some relationships to the other entities which make a chain of classes and predicates in the RDF graph. Our main approach in this paper is to discover the similarity of two entities based on similarity of paths which are emanated from them. In order to calculate semantic similarity between entities, we calculate degree of semantic similarity between paths emanating from them.

This paper takes into consideration the semantic similarity association between two entities and their similarity reflected in context. The similarity measurement is computed by combining and extending existing similarity measures and tailoring them according to the criteria induced by the application context. We will analyse the effects of applying different types of semantic similarity associations in discovering and ranking processes and figure out some directions that should be considered in designing the semantic search systems.

References

[1]

Albertoni R., De Martino M. 2008. Asymmetric and context-dependent semantic similarity among ontology instances, J. Data Semantics 10: 1--30 (2008).

Digital Library

[2]

Aleman-Meza B, Halaschek C, Arpinar IB, Sheth A. 2003. Context-aware semantic association ranking. Semantic web and databases workshop proceedings. Berlin, Germany, September 7--8 (2003).

[3]

Aleman-Meza, B. 2006. Searching and Ranking Documents based on Semantic Relationships, ICDE Workshops.

Digital Library

[4]

Anyanwu, K., Maduko, A., Sheth, A. 2007. SPARQ2L: Towards Supporting Subgraph Extraction Queries in RDF Databases. Proceedings of the WWW Conference 2007, Banff, Canada

Digital Library

[5]

Anyanwu, K., Maduko, A., and Sheth, A. 2005. SemRank: ranking complex relationship search results on the semantic web. In Proceedings of the 14th international Conference on World Wide Web, ACM.

Digital Library

[6]

Anyanwu, K. and Sheth, A. 2002. The ρ Operator: Discovering and Ranking Associations on the Semantic Web, SIGMOD, ACM.

[7]

Barton, S., Zezula, P. 2005. Rho-index - an index for graph structured data. In: 8^th International Workshop of the DELOS Network of Excellence on Digital Libraries, pp. 57--64.

[8]

Gruber, T. R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum.-Comput. Stud. Vol. 43 (1995) 907--928.

Digital Library

[9]

Han, L., Sun, L., Chen, G., and Xie, L. 2006. ADSS: an approach to determining semantic similarity. Adv. Eng. Softw. 37, 2 (Feb. 2006), 129--132.

Digital Library

[10]

Jeh, G. and Widom, J. 2002. SimRank: A measure of structural-context similarity. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada.

Digital Library

[11]

Kochut, J. and Janik, M. 2007. SPARQLeR: Extended Sparql for Semantic Association Discovery. Springer, 145--159.

[12]

Leacok, C., & Chodorov, M. 1998. Combining local context and WordNet similarity for word sense identification. In C. Fellbaum (Ed.), WordNet: An electronic lexical database and some of its applications (chap. 11). Boston: MIT Press.

[13]

Lin, D. 1998. An information-theoretic definition of similarity. In Proceedings of the 15th Int. Conf. on Machine Learning.

Digital Library

[14]

Resnik, P. 1995. Using information content to evaluate semantic similaritity. In Proceedings of the IJCAI05, pages 448--453.

Digital Library

[15]

Rodriguez M, Egenhofer M. 2003. Determining semantic similarity among entity classes from different ontologies. IEEE Trans Knowl. Data Eng; 15(2):442--56, (2003).

Digital Library

[16]

Shariatmadari, Sh., Mamat, A., Ibrahim, H., Mustapha, N. 2008. "Ranking Semantic Similarity in Semantic Web", In Poster and Demo Session Proceedings of the 7th International Semantic Web Conference, (ISWC 2008), Karlsruhe, Germany.

[17]

Shariatmadari, Sh., Mamat, A., Ibrahim, H., Mustapha, N. 2008. SwSim: Discovering Semantic Similarity Association in Semantic Web. Proceedings of the 3rd International Symposium on Information Technology 2008 (ITSim2008), IEEE Computer Society, Malaysia.

[18]

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

[19]

http://www.nlm.nih.gov/mesh

[20]

http://wordnet.princeton.edu

Cited By

Yoo HKang MOh K(2018)A Semantic Search Model Using Word Embedding, POS Tagging, and Named Entity Recognition2018 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI46756.2018.00231(1204-1209)Online publication date: Dec-2018
https://doi.org/10.1109/CSCI46756.2018.00231
Fang JGuo L(2011)Calculation of Weight of Entities in Ontologies by Using Usage Information2011 3rd International Workshop on Intelligent Systems and Applications10.1109/ISA.2011.5873390(1-4)Online publication date: May-2011
https://doi.org/10.1109/ISA.2011.5873390

Index Terms

Discovering semantic similarity association in semantic search system
1. Information systems
  1. Information retrieval

Recommendations

Ranking semantic similarity association in semantic web
ISWC-PD'08: Proceedings of the 2007 International Conference on Posters and Demonstrations - Volume 401

Discovering and ranking complex relationships in the semantic web is an important building block of semantic search applications. Although semantic web technologies define relations between objects but there are some complex (hidden) relationships that ...
Evaluating semantic similarity and relatedness over the semantic grouping of clinical term pairs

Display Omitted Objective: develop a method to quantify the similarity and relatedness of biomedical and clinical term pairs.Semantic similarity and relatedness measures exploit information extrapolated from the Unified Medical Language System.Evaluates ...
Effective semantic search using thematic similarity

Most existing semantic search systems expand search keywords using domain ontology to deal with semantic heterogeneity. They focus on matching the semantic similarity of individual keywords in a multiple-keywords query; however, they ignore the semantic ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services

December 2009

763 pages

ISBN:9781605586601

DOI:10.1145/1806338

General Chair:
Gabriele Kotsis
Johannes Kepler University Linz, Austria
,
Program Chairs:
David Taniar
Monash University, Australia
,
Eric Pardede
La Trobe University, Australia

Copyright © 2009 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

Johannes Kepler University

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

iiWAS '09

Sponsor:

iiWAS '09: 11th International Conference on Information Integration and Web-based Applications & Services

December 14 - 16, 2009

Kuala Lumpur, Malaysia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
293
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 21 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yoo HKang MOh K(2018)A Semantic Search Model Using Word Embedding, POS Tagging, and Named Entity Recognition2018 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI46756.2018.00231(1204-1209)Online publication date: Dec-2018
https://doi.org/10.1109/CSCI46756.2018.00231
Fang JGuo L(2011)Calculation of Weight of Entities in Ontologies by Using Usage Information2011 3rd International Workshop on Intelligent Systems and Applications10.1109/ISA.2011.5873390(1-4)Online publication date: May-2011
https://doi.org/10.1109/ISA.2011.5873390

View Options

Get Access

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