Enhancement of domain ontology construction using a crystallizing approach

Research highlights? We proposed an automatic ontology construction mechanism. ? The quality of constructed ontology might not be as good as expert's ontology. ? The constructed ontology can serve as an primitive ontology. ? The mechanism can accelerate the process of construction and reduce the time. An ontology is a representation model which defines domain knowledge with explicit specifications that feature interoperability between human and machine, thereby solving the problems of ambiguity and vagueness in knowledge sharing and reuse. Ontology construction is a lengthy, costly and controversial process. Hence, many studies in automatic ontology construction have emerged. In the processes of ontology construction, relations between concepts and the ways concepts are organized by their relations determine the ontology structure, which in turn affects the accuracy of domain knowledge. Consequently, concept relations exploration is the most important process of ontology construction. This study proposes a concept relation exploration approach that combines the characteristics of middle-out and top-down approaches in a process that resembles snowflakes crystallization. Based on the crystallizing concept exploration approach, this study implements an ontology construction mechanism that can automatically mine domain concepts out of domain document, determine relations between concept, and construct the domain ontology accordingly, thereby reducing cost and burden that would be incurred in a manual construction process.

[1]  Cheng-Hsin Hsu,et al.  Ontology construction for information classification , 2006, Expert Syst. Appl..

[2]  Aldo Gangemi,et al.  Ontology Learning and Its Application to Automated Terminology Translation , 2003, IEEE Intell. Syst..

[3]  Michael Uschold,et al.  Ontologies: principles, methods and applications , 1996, The Knowledge Engineering Review.

[4]  T. Kohonen Self-organized formation of topographically correct feature maps , 1982 .

[5]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[6]  Rung Ching Chen,et al.  Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency , 2008, Expert Syst. Appl..

[7]  Asunción Gómez-Pérez,et al.  An overview of methods and tools for ontology learning from texts , 2004, The Knowledge Engineering Review.

[8]  Raphael Volz,et al.  Semi-automatic Ontology Acquisition from a Corporate Intranet , 2000 .

[9]  Yau-Hwang Kuo,et al.  Automated ontology construction for unstructured text documents , 2007, Data & Knowledge Engineering.

[10]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[11]  Ricardo Baeza-Yates,et al.  Information Retrieval: Data Structures and Algorithms , 1992 .

[12]  Zongtian Liu,et al.  Ontology Learning by Clustering Based on Fuzzy Formal Concept Analysis , 2007, 31st Annual International Computer Software and Applications Conference (COMPSAC 2007).

[13]  P. C. Smits,et al.  Resource Discovery in a European Spatial Data Infrastructure , 2007 .

[14]  Ramez Elmasri,et al.  Web data cleansing and preparation for ontology extraction using WordNet , 2000, Proceedings of the First International Conference on Web Information Systems Engineering.

[15]  David Sánchez,et al.  Learning non-taxonomic relationships from web documents for domain ontology construction , 2008, Data Knowl. Eng..

[16]  Mehrnoush Shamsfard,et al.  Learning ontologies from natural language texts , 2004, Int. J. Hum. Comput. Stud..

[17]  Ahmed A. Rafea,et al.  TextOntoEx: Automatic ontology construction from natural English text , 2008, Expert Syst. Appl..

[18]  Keqing He,et al.  Towards Representing FCA-based Ontologies in Semantic Web Rule Language , 2006, The Sixth IEEE International Conference on Computer and Information Technology (CIT'06).

[19]  J.M. Park,et al.  Product Ontology Construction from Engineering Documents , 2008, 2008 International Conference on Smart Manufacturing Application.

[20]  Elisabeth Métais,et al.  Building and maintaining ontologies: a set of algorithms , 2004, Data Knowl. Eng..

[21]  Asunción Gómez-Pérez,et al.  Overview and analysis of methodologies for building ontologies , 2002, The Knowledge Engineering Review.

[22]  Dieter Fensel,et al.  Ontology-Based Knowledge Management , 2002, Computer.

[23]  Carole A. Goble,et al.  Learning domain ontologies for semantic Web service descriptions , 2005, J. Web Semant..

[24]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[25]  M. F. Porter,et al.  An algorithm for suffix stripping , 1997 .

[26]  Huan-Chung Li,et al.  Automated Food Ontology Construction Mechanism for Diabetes Diet Care , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[27]  Sean Bechhofer,et al.  GOHSE: Ontology driven linking of biology resources , 2006, J. Web Semant..

[28]  Yurdaer N. Doganata,et al.  Glossary extraction and utilization in the information search and delivery system for IBM Technical Support , 2004, IBM Syst. J..

[29]  Mariana Gosnell Ice: The Nature, the History, and the Uses of an Astonishing Substance , 2005 .

[30]  Antonio De Nicola,et al.  A software engineering approach to ontology building , 2009, Inf. Syst..

[31]  Slava M. Katz,et al.  Technical terminology: some linguistic properties and an algorithm for identification in text , 1995, Natural Language Engineering.

[32]  Nigel Shadbolt,et al.  APECKS: using and evaluating a tool for ontology construction with internal and external KA support , 2002, Int. J. Hum. Comput. Stud..

[33]  Jane Hunter,et al.  Towards a Core Ontology for Information Integration , 2003, J. Digit. Inf..

[34]  Steffen Staab,et al.  Knowledge Processes and Ontologies , 2001, IEEE Intell. Syst..

[35]  Gerda Ruge,et al.  Automatic Detection of Thesaurus relations for Information Retrieval Applications , 1997, Foundations of Computer Science: Potential - Theory - Cognition.

[36]  Feng Luo,et al.  Ontology construction for information selection , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..