An approximation to the computational theory of perceptions using ontologies

Highlights? CWP provides a framework to develop systems that operate with the meaning of NL expressions. ? A high level ontology is defined to describe the Granular Linguistic Model of Phenomena. ? Application ontologies represent complex phenomena and linguistic descriptions. ? Natural language sentences are obtained after instantiating application ontologies. New technologies allow users to access huge amounts of data about phenomena in their environment. Nevertheless, linguistic description of these available data requires that human experts interpret them highlighting the relevant details and hiding the irrelevant ones. Experts use their personal experience on the described phenomenon and in using the flexibility of natural language to create their reports. In the research line of Computing with Words and Perceptions, this paper deals with the challenge of using ontologies to create a computational representation of the expert's knowledge including his/her experience on both the context of the analyzed phenomenon and his/her personal use of language in that specific context. The proposed representation takes as basis the Granular Linguistic Model of a Phenomenon previously proposed by two of the authors. Our approach is explained and demonstrated using a series of practical prototypes with increasing degree of complexity.

[1]  Alberto Bugarín,et al.  On the role of fuzzy quantified statements in linguistic summarization of data , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[2]  Jerry M. Mendel,et al.  Foreword to the Special Section on Computing With Words , 2010, IEEE Trans. Fuzzy Syst..

[3]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[4]  Janusz Kacprzyk,et al.  A Fuzzy Logic Based Approach to Linguistic Summaries of Databases , 2000 .

[5]  Pragya Agarwal,et al.  Ontological considerations in GIScience , 2005, Int. J. Geogr. Inf. Sci..

[6]  Hanêne GHORBEL,et al.  Fuzzy Protégé for Fuzzy Ontology Models , 2009 .

[7]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[8]  Gracián Triviño,et al.  Automatic linguistic description about relevant features of the Mars' surface , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[9]  Chang-Shing Lee,et al.  Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition , 2007, Expert Syst. Appl..

[10]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[11]  Chang-Shing Lee,et al.  Ontology-based fuzzy support agent for ship steering control , 2009, Expert Syst. Appl..

[12]  Mariano Fernández-López,et al.  Ontological Engineering , 2003, Encyclopedia of Database Systems.

[13]  Lotfi A. Zadeh,et al.  A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[14]  Chang-Shing Lee,et al.  Ontology-based fuzzy event extraction agent for Chinese e-news summarization , 2003, Expert Syst. Appl..

[15]  Gracián Triviño,et al.  Combining Semantic Web technologies and Computational Theory of Perceptions for text generation in financial analysis , 2010, International Conference on Fuzzy Systems.

[16]  Lotfi A. Zadeh,et al.  From Computing with Numbers to Computing with Words - from Manipulation of Measurements to Manipulation of Perceptions , 2005, Logic, Thought and Action.

[17]  Umberto Straccia,et al.  Fuzzy Ontology Representation using OWL 2 , 2010, Int. J. Approx. Reason..

[18]  Silvia Calegari,et al.  Granular computing applied to ontologies , 2010, Int. J. Approx. Reason..

[19]  Daniel Sánchez,et al.  Fuzzy cardinality based evaluation of quantified sentences , 2000, Int. J. Approx. Reason..

[20]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[21]  Ju Wang,et al.  Reasoning and change management in modular fuzzy ontologies , 2011, Expert Syst. Appl..

[22]  Fernando Bobillo,et al.  DeLorean: A reasoner for fuzzy OWL 2 , 2012, Expert Syst. Appl..

[23]  L. Zadeh A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[24]  Heiner Stuckenschmidt,et al.  Handbook on Ontologies , 2004, Künstliche Intell..

[25]  R. Doyle The American terrorist. , 2001, Scientific American.

[26]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[27]  M. Amparo Vila,et al.  Ontologies versus relational databases: are they so different? A comparison , 2012, Artificial Intelligence Review.

[28]  Alejandro Sanchez,et al.  Linguistic description of traffic in a roundabout , 2010, International Conference on Fuzzy Systems.

[29]  Ronald R. Yager,et al.  A new approach to the summarization of data , 1982, Inf. Sci..

[30]  Lotfi A. Zadeh,et al.  Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift , 2008, IEEE Computational Intelligence Magazine.