Research on domain ontology in different granulations based on concept lattice

Knowledge-Based Systems

Volumn 27, Issue , 2012, Pages 152-161

  Kang, Xiangping ^a,b   Li, Deyu ^a,b   Wang, Suge ^a,b  

^a SHANXI UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Concept lattice; Granular computing; Ontology building; Ontology connection; Ontology merging

Indexed keywords

COMPLEX DOMAINS; CONCEPT LATTICES; DEGREE OF CONNECTION; DOMAIN EXPERTS; DOMAIN ONTOLOGIES; MULTI-GRANULATIONS; ONTOLOGY BUILDING; ONTOLOGY LEARNING; ONTOLOGY MERGING; RESEARCH MODELS; SIMILARITY MODELS; SIMILARITY OF ONTOLOGIES; UNIFORM TECHNOLOGY;

GRANULAR COMPUTING; GRANULATION; INFORMATION ANALYSIS; MERGING;

ONTOLOGY;

EID: 84855963250     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2011.09.016     Document Type: Article

References (43)

1. T. Gruber Translation approach to portable ontology specifications Knowledge Acquisition 5 1993 199 220
2. W.N. Borst, Construction of engineering ontologies for knowledge sharing and reuse. Ph.D. Thesis, University of Twente, Enschede, 1997.
3. C.S. Lee, and M.H. Wang Ontology-based computational intelligent multi-agent and its application to CMMI assessment Applied Intelligence 30 3 2009 203 219
4. C.S. Lee, M.H. Wang, and J.J. Chen Ontology-based intelligent decision support agent for CMMI project monitoring and control International Journal of Approximate Reasoning 48 1 2008 62 76
5. S. Calegari, and F. Farina Fuzzy ontologies and scale-free networks analysis International Journal of Computer Science & Applications 4 2 2007 125 144
6. H. Alani, S. Kim, D. Millard, M. Weal, W. Hall, P. Lewis, and N. Shadbolt Automatic ontology-based knowledge extraction from web documents IEEE Intelligent Systems 18 1 2003 14 21
7. R. Gacitua, P. Sawyer, and P. Rayson A flexible framework to experiment with ontology learning techniques Knowledge-Based Systems 21 3 2008 192 199
8. T.T. Quan, S.C. Hui, and A.C.M. Fong Automatic fuzzy ontology generation for semantic help-desk support IEEE Transactions on Industrial Informatics 2 3 2006 155 164
9. R.C. Chen, J.Y. Liang, and R.H. Pan Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency Expert Systems with Applications 34 1 2008 488 501
10. J. Köhler, S. Philippi, M. Specht, and A. Rüegg Ontology based text indexing and querying for the semantic web Knowledge-Based Systems 19 8 2006 744 754
11. A. Formica Ontology-based concept similarity in formal concept analysis Information Sciences 176 18 2006 2624 2641
12. R. Wille, Restructuring lattice theory: an approach based on hierarchies of concepts. in: I. Rival (ed.), Ordered Sets, Dordrecht, Reidel, 1982, pp. 445-470.
13. B. Ganter, and R. Wille Formal Concept Analysis: Mathematical Foundations 1999 Springer-Verlag Berlin
14. L. Wei, and J.J. Qi Relation between concept lattice reduct and rough set reduct Knowledge-Based Systems 23 8 2010 934 938
15. L. Wang, and X. Liu A new model of evaluating concept similarity Knowledge-Based Systems 21 8 2008 842 846
16. A. Burusco, and R. Fuentes-Gonzalez Construction of the L-fuzzy concept lattice Fuzzy Sets and Systems 97 1 1998 109 114
17. J.S. Mi, Y. Leung, and W.Z. Wu Approaches to attribute reduct in concept lattices induced by axialities Knowledge-Based Systems 23 6 2010 504 511
18. J. Medina, M. Ojeda-Aciego, and J. Ruiz-Calviño Formal concept analysis via multi-adjoint concept lattices Fuzzy Sets and Systems 160 2 2009 130 144
19. L. Li, and J. Zhang Attribute reduction in fuzzy concept lattices based on the T implication Knowledge-Based Systems 23 6 2010 497 503
20. X. Wang, and W.X. Zhang Relations of attribute reduction between object and property oriented concept lattices Knowledge-Based Systems 21 5 2008 398 403
21. B. Zupa, and M. Bohance Learning by discovering concept hierarchies Artificial Intelligence 109 1-2 1999 211 242
22. U. Dekel, Revealing Java class structure with concept lattices. Master's Thesis. Technion-Israel Institute of Technology, 2003.
23. P. Valtchev, R. Missaoui, R. Godin, and M. Meridji Generating frequent itemsets incrementally: two novel approaches based on Galois lattice theory Journal of Experimental and Theoretical Artificial Intelligence 14 2-3 2002 115 142
24. P. Cimiano, G. Stumme, A. Hotho, J. Tane. Conceptual knowledge processing with formal concept analysis and ontologies, in: Proceedings of the Second International Conference on Formal Concept Analysis (ICFCA 04), Springer, 2004, pp. 189-207.
25. P. Cimiano, S. Staab, J. Tane, Automatic acquisition of taxonomies from text: FCA meets NLP, in: Proceedings of the International Workshop on Adaptive Text Extraction and Mining, 2003, pp. 10-17.
26. G. Tao Using formal concept analysis for ontology structuring and building 2003 ICIS, Nanyang Technological University
27. H.M. Haav, A semi-automatic method to ontology design by using FCA, in: V. Snasel, R. Belohlavek (Eds.), Concept Lattices and their Applications, Proceedings of the 2nd International CLA Workshop, TU of Ostrava, 2004, pp. 13-25.
28. H.M. Haav, An application of inductive concept analysis to construction of domain-specific ontologies, in: B. Thalheim, Gunar Fiedler (Eds.), Emerging Database Research in East Europe, Proceedings of the Pre-conference Workshop of VLDB 2003, Computer Science Reports, Brandenburg University of Technology at Cottbus, 14(3) (2003) 63-67.
29. M. Obitko, V. snasel, J. Smid. Ontology Design with Formal Concept Analysis, Edited by Vaclav Snasel, Radim Belohlavek. in: Proceedings of the CLA 2004 International Workshop on Concept Lattices and their Applications, Ostrava, Czech Republic, Sep. 2004, pp. 111-119.
30. G. Stumme, M. Adche, A FCA-Merge: bottom-up merging of ontologies. in: Proceedings of the Seventeenth International Conference on Artificial Intelligence (IJCAI'01), Seattle, USA, 2001, pp. 225-230.
31. B. Ganter, and G. Stumme Creation and merging of ontology top-levels A.de. Moor, W. Lex, B. Ganter, Conceptual Structures for Knowledge Creation and Communication. Proc. ICCS'03 LNAI 2746 2003 Springer Heidelberg 131 145
32. R.C. Chen, C.T. Bau, and C.J. Yeh Merging domain ontologies based on the WordNet system and fuzzy formal concept analysis techniques Applied Soft Computing 11 2 2011 1908 1923
33. J.Y. Liang, and Z.Z. Shi The information entropy, rough entropy and knowledge granulation in rough set theory, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 12 1 2004 37 46
34. J.B. Tenenbaum, and T.L. Griffiths Generalization, similarity, and Bayesian inference Behavioral and Brain Sciences 24 2001 629 640
35. A. Tversky Features of similarity Psychological Review 84 4 1977 327 352
36. M.A. Rodriguez, and M.J. Egenhofer Determining semantic similarity among entity classes from different ontologies IEEE Transactions on Knowledge and Data Engineering 15 2003 442 456
37. L.A. Zadeh Fuzzy sets Information and Control 8 1966 338 353
38. H.S. Lee An optimal algorithm for computing the max-min transitive closure of a fuzzy similarity matrix Fuzzy Sets and Systems 123 2001 129 136
39. D. Dubois, and H. Prade On possibility theory, formal concept analysis and granulation Survey. Appl. Comput. Math. 10 1 2011 10 19
40. T.Y. Lin Granular computing on binary relations I: data mining and neighborhood systems, II: rough sets representations and belief functions L. Polkowski, A. Skowron, Rough Sets in Knowledge Discovery 1 1998 Physica-Verlag Heidelberg 107 140
41. Y.Y. Yao, Granular computing: basic issues and possible solutions, in: Proceedings of the 5th Joint Conference on Information Sciences, 2000, pp. 186-189.
42. S.B. Roh, S.K. Oh, and W. Pedrycz A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering Knowledge-Based Systems 23 3 2010 202 219
43. J. Zhao, and L. Liu Construction of concept granule based on rough set and representation of knowledge-based complex system Knowledge-Based Systems 24 6 2011 809 815