Semantic relationships and approximations of sets: An ontological graph based approach

Approximation of sets is a fundamental notion of rough set theory (RST) proposed by Z. Pawlak. In a classic approach, considered in RST, approximation of sets is defined on the basis of an indiscernibility relation between objects in some universe of discourse. However, approximations of sets become problematic in many cases, especially, if attribute values describing objects are symbolical (e.g., words, terms, linguistic concepts, etc.). In fact, such a situation is natural in human cognition and description of the real world. Different approaches perfecting rough set theory in this area have been proposed in the literature. One of them is based on incorporating ontologies enabling us to add some new, valuable knowledge which can be used in data analysis, rule generation, reasoning, etc. In the paper, we propose to use ontological graphs in determining approximations of sets and show how ontological graphs change a look at them. The presented approach refers to a general trend in computations proposed by L. Zadeh and called “computing with words”.

[1]  Roger Chaffin,et al.  The nature of semantic relations: a comparison of two approaches , 1989 .

[2]  Martha Walton Evens Relational Models of the Lexicon: Representing Knowledge in Semantic Networks , 2009 .

[3]  Michael Specht,et al.  Ontology based text indexing and querying for the semantic web , 2006, Knowl. Based Syst..

[4]  Rashmi Data Mining: A Knowledge Discovery Approach , 2012 .

[5]  Krzysztof Pancerz,et al.  Dominance-Based Rough Set Approach for decision systems over ontological graphs , 2012, 2012 Federated Conference on Computer Science and Information Systems (FedCSIS).

[6]  M. Orosz,et al.  SFINKS: Secure Focused Information, News, and Knowledge Sharing , 2008, 2008 IEEE Conference on Technologies for Homeland Security.

[7]  Veda C. Storey,et al.  Understanding semantic relationships , 1993, The VLDB Journal.

[8]  Michael L. Brodie On conceptual modelling - perspectives from artificial intelligence, databases and programming languages , 1984, Topics in information systems.

[9]  Timothy W. Finin,et al.  Enabling Technology for Knowledge Sharing , 1991, AI Mag..

[10]  Krzysztof Pancerz,et al.  Matching ontological subgraphs to concepts: A preliminary rough set approach , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[11]  Krzysztof Pancerz Toward Information Systems over Ontological Graphs , 2012, RSCTC.

[12]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[13]  Douglas Herrmann,et al.  A Taxonomy of Part-Whole Relations , 1987, Cogn. Sci..

[14]  Jessica L. Milstead,et al.  Standards for Relationships between Subject Indexing Terms , 2001 .

[15]  Jan Komorowski,et al.  A Rough Set Framework for Learning in a Directed Acyclic Graph , 2002, Rough Sets and Current Trends in Computing.

[16]  Daniel Vanderpooten,et al.  A Generalized Definition of Rough Approximations Based on Similarity , 2000, IEEE Trans. Knowl. Data Eng..

[17]  Michael L. Brodie,et al.  On Conceptual Modelling , 1984, Topics in Information Systems.

[18]  Ronald J. Brachman,et al.  What IS-A Is and Isn't: An Analysis of Taxonomic Links in Semantic Networks , 1983, Computer.

[19]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[20]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[21]  D. Cruse On the transitivity of the part-whole relation , 1979, Journal of Linguistics.

[22]  KöhlerJacob,et al.  Ontology based text indexing and querying for the semantic web , 2006 .