Extended TvX: A New Method Feature Based Semantic Similarity for Multiple Ontology

Semantic similarity between the terms is the main phase in information retrieval and information integration, which requires semantic content matching. Semantic similarity function is important in psychology, artificial intelligence and cognitive science. The problem of integrating various sources is the matching between ontological concepts. In this paper, we proposed to develop this method by analyzing the semantic similarity between the modeled taxonomical knowledge and features in different ontology. This paper contains a review on semantic similarity and multiple ontology that focuses on the feature-based approach. Besides that, we proposed a method, namely a semantic similarity that overcomes the limitation of different features of terms compared. As a result, we are able to develop a better method that improves the accuracy of the similarity measurement.

[1]  Roy Rada,et al.  Development and application of a metric on semantic nets , 1989, IEEE Trans. Syst. Man Cybern..

[2]  Domenico Talia,et al.  SECCO: On Building Semantic Links in Peer-to-Peer Networks , 2009, J. Data Semant..

[3]  David Sánchez,et al.  Automatic extraction of acronym definitions from the Web , 2011, Applied Intelligence.

[4]  Luigi Iannone,et al.  An algorithm based on counterfactuals for concept learning in the Semantic Web , 2005, Applied Intelligence.

[5]  Pedro M. Domingos,et al.  Ontology Matching: A Machine Learning Approach , 2004, Handbook on Ontologies.

[6]  David Sánchez,et al.  A semantic similarity method based on information content exploiting multiple ontologies , 2013, Expert Syst. Appl..

[7]  David Sánchez,et al.  Semantic similarity estimation from multiple ontologies , 2012, Applied Intelligence.

[8]  Yun Tian,et al.  Improvement of semantic similarity algorithm based on WordNet , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[9]  Hisham Al-Mubaid,et al.  Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  David Sánchez,et al.  Enabling semantic similarity estimation across multiple ontologies: An evaluation in the biomedical domain , 2012, J. Biomed. Informatics.

[11]  Graeme Hirst,et al.  Evaluating WordNet-based Measures of Lexical Semantic Relatedness , 2006, CL.

[12]  A. Tversky.  Features of Similarity , 1977 .

[13]  Safaai Deris,et al.  Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data , 2013, Comput. Biol. Medicine.

[14]  Euripides G. M. Petrakis,et al.  X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies , 2006, J. Digit. Inf. Manag..

[15]  G. Aghila,et al.  COSS: Cross Ontology Semantic Similarity measure — An information content based approach , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[16]  J. Akilandeswari,et al.  A Survey on Semantic Similarity Measure , 2014 .

[17]  Max J. Egenhofer,et al.  Determining Semantic Similarity among Entity Classes from Different Ontologies , 2003, IEEE Trans. Knowl. Data Eng..

[18]  Euripides G. M. Petrakis,et al.  Information Retrieval by Semantic Similarity , 2006, Int. J. Semantic Web Inf. Syst..

[19]  David Sánchez,et al.  Ontology-based semantic similarity: A new feature-based approach , 2012, Expert Syst. Appl..

[20]  David Sánchez,et al.  Content annotation for the semantic web: an automatic web-based approach , 2011, Knowledge and Information Systems.