A Framework to Normalize Ontology Representation for Stable Measurement

Ontology measurement is an important challenge in the field of knowledge management in order to manage the development of ontology based systems and reduce the risk of project failure. Effective ontology measurement is the precondition on which the meaningful and useful ontology evaluation can be made. We propose a framework to normalize representation of ontologies for their stable measurement, where the semantic enriched representation model (SERM) is proposed as the unique representation for ontologies. By the normalization framework, we provide a four-step procedure to extract ontology entities and calculate measures based on SERM model. Both the theoretical analysis and the experimental results show that our framework is effective and useful to perform stable ontology measurement. It is suitable to measure more expressive ontologies. This framework enables users to perform automatic ontology measurement without much expertise knowledge about ontology programming and reasoning.

[1]  Asunción Gómez-Pérez,et al.  Ontological Engineering: With Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web , 2004, Advanced Information and Knowledge Processing.

[2]  Hai Zhuge,et al.  Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning , 2009, IEEE Transactions on Knowledge and Data Engineering.

[3]  Liana Razmerita An Ontology-Based Framework for Modeling User Behavior—A Case Study in Knowledge Management , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Sebastian Rudolph,et al.  Description Logic Reasoning with Decision Diagrams: Compiling SHIQ to Disjunctive Datalog , 2008, SEMWEB.

[5]  Elena García Barriocanal,et al.  Knowledge representation issues in ontology-based clinical Knowledge Management systems , 2009, Int. J. Technol. Manag..

[6]  Jordi Conesa,et al.  Exploiting the Semantic Web to Represent Information from On-line Collaborative Learning , 2012, Int. J. Comput. Intell. Syst..

[7]  Yinglong Ma,et al.  Stable cohesion metrics for evolving ontologies , 2011, J. Softw. Maintenance Res. Pract..

[8]  B. Kitchenham,et al.  Measurement Modeling Technology , 2003, IEEE Softw..

[9]  Gang Xu,et al.  Inconsistent ontology revision based on ontology constructs , 2010, Expert Syst. Appl..

[10]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[11]  Elena Paslaru Bontas Simperl,et al.  Reusing ontologies on the Semantic Web: A feasibility study , 2009, Data Knowl. Eng..

[12]  Letha H. Etzkorn,et al.  Complexity metrics for ontology based information , 2009, Int. J. Technol. Manag..

[13]  Yinglong Ma Towards Stable Semantic Ontology Measurement , 2010, ISWC Posters&Demos.

[14]  Timothy W. Finin,et al.  Swoogle: a search and metadata engine for the semantic web , 2004, CIKM '04.

[15]  Jos de Bruijn,et al.  Information Integration with Ontologies: Experiences from an Industrial Showcase , 2005 .

[16]  Yinglong Ma,et al.  Semantic oriented ontology cohesion metrics for ontology-based systems , 2010, J. Syst. Softw..

[17]  Letha H. Etzkorn,et al.  Indicating ontology data quality, stability, and completeness throughout ontology evolution , 2007, J. Softw. Maintenance Res. Pract..

[18]  Ana Maria de Carvalho Moura,et al.  Integrating Ontologies Based on P2P Mappings , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  Heon Young Yeom,et al.  Cohesion and coupling metrics for ontology modules , 2011, Inf. Technol. Manag..

[20]  Riichiro Mizoguchi,et al.  Tutorial on ontological engineering: part 3: Advanced course of ontological engineering , 2004 .

[21]  Asunción Gómez-Pérez,et al.  ONTOMETRIC: A Method to Choose the Appropriate Ontology , 2004, J. Database Manag..

[22]  Weichang Du,et al.  A Metric Suite for Evaluating Cohesion and Coupling in Modular Ontologies , 2010, WoMO.

[23]  Riichiro Mizoguchi,et al.  Part 3: Advanced course of ontological engineering , 2004, New Generation Computing.

[24]  Matteo Gaeta,et al.  Ontology Extraction for Knowledge Reuse: The e-Learning Perspective , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[25]  Hongyu Zhang,et al.  Measuring design complexity of semantic web ontologies , 2010, J. Syst. Softw..

[26]  Raymond Y. K. Lau,et al.  Fuzzy Ontology Mining and Semantic Information Granulation for Effective Information Retrieval Decision Making , 2011, Int. J. Comput. Intell. Syst..