Semi-automatic Construction of Ontology-based CBR System for Knowledge Integration
In order to integrate knowledge in heterogeneous
case-based reasoning (CBR) systems, ontology-based CBR system
has become a hot topic. To solve the facing problems of
ontology-based CBR system, for example, its architecture is
nonstandard, reusing knowledge in legacy CBR is deficient, ontology
construction is difficult, etc, we propose a novel approach for
semi-automatically construct ontology-based CBR system whose
architecture is based on two-layer ontology. Domain knowledge
implied in legacy case bases can be mapped from relational database
schema and knowledge items to relevant OWL local ontology
automatically by a mapping algorithm with low time-complexity. By
concept clustering based on formal concept analysis, computing
concept equation measure and concept inclusion measure, some
suggestions about enriching or amending concept hierarchy of OWL
local ontologies are made automatically that can aid designers to
achieve semi-automatic construction of OWL domain ontology.
Validation of the approach is done by an application example.
[1] Agnar Aamodt, Enric Plaza, "Case-Based Reasoning:Foundational Issues,
Methodological Variations, and System Approaches," AI
Communications. IOS Press.1994, Vol.7, pp. 39-59
[2] David W. Aha, "The omnipresence of case-based reasoning in science and
application," Knowledge-Based System. 1998, Vol.11, pp. 261-273.
[3] B. Diaz-Agudo and P. A. Gonzalez-Calero, "An Architecture for
Knowledge In-tensive CBR Systems," In Proceedings of the 5th
European Workshop on Advances in Case-Based Reasoning (EWCBR'00),
volume 1898 of LNCS, pages Springer-Verlag, 2000. , pp. 37-48.
[4] B. Diaz-Agudo and P. A. Gonzalez-Calero, " Knowledge Intensive CBR
through Ontologies," In Procs of the 6ht UK CBR Workshop. 2001.
[5] Wache, H., V ogele, T., Visser, U., Stuckenschmidt, H., Schuster, G.,
Neumann, H., and Hubner, S, "Ontology-based integration of information
- a survey of existing approaches," In IJCAI-01 Workshop: Ontologies
and Information Sharing, 2001, pp. 108--117.
[6] Junjie Gao, Guishi Deng. , "The research of applying domain ontology to
case-based reasoning system," In Proceedings of International
Conference on Services Systems and Services Management, Beijing, 2005,
pp.1113 - 1117
[7] Man Li, Xiao-yong Du, "Learning ontology from relational database,"
Proceedings of the Fourth International Conference on Machine
Learning and Cybernetics, Guangzhou, 18-21 August 2005.
pp.3410-3415
[8] I. Astrova, "Extracting Ontologies from Relational Databases," In
Proceedings of the 22nd IASTED International Conference on Databases
and Applications (DBA), 2004, pp. 56-61
[9] Habegger,B, "Mapping a database into an ontology: an interactive
relational learning approach," In 2007 Proceedings of the 23rd
International Conference on Data Engineering pp.1443-1447
[10] Zhuoming Xu, Shichao Zhang,Yisheng Dong, "Mapping between
Relational Database Schema and OWL Ontology for Deep Annotation,"
In 2006 Proceedings of International Conference on Web Intelligence. pp.
548-552
[11] Ehrig, M., Staab, S, "QOM - quick ontology mapping," In The Semantic
Web - ISWC 2004. LNCS 3298. Springer-Verlag, Berlin Heidelberg New
York pp.683-696
[12] Ganter, B., Wille, R., Formal Concept Analysis: Mathematical
Foundations, Springer-Verlag, New York (1999)
[13] Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L, "Fast
computation of concept lattices using data mining techniques," In 2000
Proceedings of 7th International Workshop on Knowledge
Representation Meets Databases. Berlin, Germany pp.129-139.
[14] http://soureforge.net/projects/conexp
[15] http://www.w3.org/TR/2008/WD-owl11-syntax-20080108/
[1] Agnar Aamodt, Enric Plaza, "Case-Based Reasoning:Foundational Issues,
Methodological Variations, and System Approaches," AI
Communications. IOS Press.1994, Vol.7, pp. 39-59
[2] David W. Aha, "The omnipresence of case-based reasoning in science and
application," Knowledge-Based System. 1998, Vol.11, pp. 261-273.
[3] B. Diaz-Agudo and P. A. Gonzalez-Calero, "An Architecture for
Knowledge In-tensive CBR Systems," In Proceedings of the 5th
European Workshop on Advances in Case-Based Reasoning (EWCBR'00),
volume 1898 of LNCS, pages Springer-Verlag, 2000. , pp. 37-48.
[4] B. Diaz-Agudo and P. A. Gonzalez-Calero, " Knowledge Intensive CBR
through Ontologies," In Procs of the 6ht UK CBR Workshop. 2001.
[5] Wache, H., V ogele, T., Visser, U., Stuckenschmidt, H., Schuster, G.,
Neumann, H., and Hubner, S, "Ontology-based integration of information
- a survey of existing approaches," In IJCAI-01 Workshop: Ontologies
and Information Sharing, 2001, pp. 108--117.
[6] Junjie Gao, Guishi Deng. , "The research of applying domain ontology to
case-based reasoning system," In Proceedings of International
Conference on Services Systems and Services Management, Beijing, 2005,
pp.1113 - 1117
[7] Man Li, Xiao-yong Du, "Learning ontology from relational database,"
Proceedings of the Fourth International Conference on Machine
Learning and Cybernetics, Guangzhou, 18-21 August 2005.
pp.3410-3415
[8] I. Astrova, "Extracting Ontologies from Relational Databases," In
Proceedings of the 22nd IASTED International Conference on Databases
and Applications (DBA), 2004, pp. 56-61
[9] Habegger,B, "Mapping a database into an ontology: an interactive
relational learning approach," In 2007 Proceedings of the 23rd
International Conference on Data Engineering pp.1443-1447
[10] Zhuoming Xu, Shichao Zhang,Yisheng Dong, "Mapping between
Relational Database Schema and OWL Ontology for Deep Annotation,"
In 2006 Proceedings of International Conference on Web Intelligence. pp.
548-552
[11] Ehrig, M., Staab, S, "QOM - quick ontology mapping," In The Semantic
Web - ISWC 2004. LNCS 3298. Springer-Verlag, Berlin Heidelberg New
York pp.683-696
[12] Ganter, B., Wille, R., Formal Concept Analysis: Mathematical
Foundations, Springer-Verlag, New York (1999)
[13] Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L, "Fast
computation of concept lattices using data mining techniques," In 2000
Proceedings of 7th International Workshop on Knowledge
Representation Meets Databases. Berlin, Germany pp.129-139.
[14] http://soureforge.net/projects/conexp
[15] http://www.w3.org/TR/2008/WD-owl11-syntax-20080108/
@article{"International Journal of Information, Control and Computer Sciences:64986", author = "Junjie Gao and Guishi Deng", title = "Semi-automatic Construction of Ontology-based CBR System for Knowledge Integration", abstract = "In order to integrate knowledge in heterogeneous
case-based reasoning (CBR) systems, ontology-based CBR system
has become a hot topic. To solve the facing problems of
ontology-based CBR system, for example, its architecture is
nonstandard, reusing knowledge in legacy CBR is deficient, ontology
construction is difficult, etc, we propose a novel approach for
semi-automatically construct ontology-based CBR system whose
architecture is based on two-layer ontology. Domain knowledge
implied in legacy case bases can be mapped from relational database
schema and knowledge items to relevant OWL local ontology
automatically by a mapping algorithm with low time-complexity. By
concept clustering based on formal concept analysis, computing
concept equation measure and concept inclusion measure, some
suggestions about enriching or amending concept hierarchy of OWL
local ontologies are made automatically that can aid designers to
achieve semi-automatic construction of OWL domain ontology.
Validation of the approach is done by an application example.", keywords = "OWL ontology, Case-based Reasoning, FormalConcept Analysis, Knowledge Integration", volume = "4", number = "3", pages = "603-7", }