2.1 Source of Uncertainty
Web is consisted of immense amount of data. Information retrieval from this extremely huge source is not immune for inconsistencies or uncertaintities. Uncertainty or imprecision on the web could be due to 2 main factors: First, even in extremely accurate measurements we are uncertain about the implications. Second, the human perception [9] is fundamentally unable to conduct completely accurate measurements.
2.2 Extensions to deal with Uncertainty
To deal with uncertainty many extensions have been proposed on OWL and Description knowledge languages. The proposed extensions could be divided to the following three categories:
1. Probabilistic Extension
2. Possibilistic Extension
3. Fuzzy Extension
Among the three types of extensions we are going to focus on fuzzy extensions which has been the most active category among researchers [3, 4, 5, 6].There is a fundamental difference in the semantics of fuzzy logic and probabilistic logic. In fuzzy logic, a statement can be true to a certain extent or an entity belongs to a class to a certain degree. This degree is assumed to be known with certainty. In probabilistic reasoning, there is a probability that a statement is true or false, but the statement itself is either true or false, but neither both nor something in between. Hence fuzzy logic sees the world as continuous instead of binary, while probabilistic logics make a claim about the randomness of the world or the observer’s state of certainty [8].
2006: In 2006 Fuzzy OWL was proposed in the National Technical University of Athens [1]. Fuzzy OWL is capable of capturing and reasoning about knowledge using their reasoning platform, Fuzzy Reasoning Engine (FiRE). Fuzzy OWL represents fuzzy classes and properties. A fuzzy class is defined by a membership function that returns the membership degree between [0,1] for a given object. Fuzzy OWL uses crisp OWL’s Syntax for class and property axioms and definitions, and FiRE uses RACER DL [2] engine syntax.
2005: Semantic Web Rule Language (SWRL) is a proposal that combines OWL (DL and Lite) with the Rule Markup Language (RuleML). Fuzzy-SWRL (f-SWRL) is a fuzzy extension of Semantic Web Rule Language [7]. In both the antecedent and consequent of SWRL rules atoms can have weights between [0, 1]. f-SWRL provides a powerful and flexible knowledge representation and very convenient for multimedia as well as semantic web.
2.3 Application of Fuzzy in Semantic Web Systems
2007: A recommender system using temporal ontologies is proposed in [10]. The agents in the system provide preference and uncertain lists to the user. The uncertain list is the same type of information in the preference list but the acquired data is based on the known products in the agent’s ontology. The agent’s ontology contains the previous feedbacks about the products.
2005: Haibin and Yan proposed a framework called soft Semantic Web Services agent (soft SWS agent) [11] providing high quality semantic web services using fuzzy neural networks with genetic algorithms. The core of soft SWS agent is the intelligent inference engine (IIE) which uses a four layer architecture fuzzy neural network. Linguistic variables in layer one change to output variables in layer four after the fuzzy process in the layered architecture.
[12] introduces a concept-matching information retrieval system that is capable of “retrieving web pages that are conceptually related to the implicit concepts of the query”. The system uses Fuzzy interrelations and Synonymy-Based Concept Representation Model (FIS-CRM) to extract the concepts. The vectors in FIS-CRM are fuzzy values representing “concept” occurrence instead of term occurrence.
Reference
[1] Stoilos G., Simou N., Stamou G., Kollias S. Uncertainty and the Semantic Web, IEEE Intelligent Systems, 2006
[2] www.sts.tu-harburg.de/~r.f.moeller/racer
[3] P. Vojtas. Fuzzy logic programming. Fuzzy Sets and Systems, 124:361-370, 2001.
[4] R. Ebrahim. Fuzzy logic programming. Fuzzy Sets and Systems, 117:215- 230, 2001.
[5] Cristinel Mateis. Extending disjunctive logic programming by t-norms. In LPNMR '99: Proceedings of the 5th International Conference on Logic Programming and Nonmonotonic Reasoning, pages 290-304, London, UK, 1999. Springer-Verlag.
[6] C. V. Damasio, L. M. Pereira. Antitonic logic programs. In 6th International Conference on Logic Programming and Nonmonotonic Reasoning, 2001.
[7] Jeff Z. Pan, Giorgos Stamou, Vassilis Tzouvaras, and Ian Horrocks. f- SWRL: A Fuzzy Extension of SWRL. In Proc. of the International Conference on Artificial Neural Networks (ICANN 2005), Special section on "Intelligent multimedia and semantics", 2005.
[8] Christopher Thomas and Amit Sheth. On the Expressiveness of the Languages for the Semantic Web – Making a Case for ‘A Little More’
[9] Lotfi A. Zadeh, Toward a perception-based theory of probabilistic reasoning with imprecise probabilities, In Journal of Statistical Planning and Inference 105 (2002) 233-264
[10] Trust based Recommender System for the Semantic Web
[11] Wang h., Zhang Y. Extensible Soft Semantic Web Services Agent
[12] Garces J., Olivas P. J., Romero F.P. Concept-Matching IR System Versus Word-Matching Information Retrieval Systems: Considering Fuzzy Interrelations for Indexing Web Pages. Journal of the American society for information science and technology, 57 (4). Pp. 564-576, 2006
Web is consisted of immense amount of data. Information retrieval from this extremely huge source is not immune for inconsistencies or uncertaintities. Uncertainty or imprecision on the web could be due to 2 main factors: First, even in extremely accurate measurements we are uncertain about the implications. Second, the human perception [9] is fundamentally unable to conduct completely accurate measurements.
2.2 Extensions to deal with Uncertainty
To deal with uncertainty many extensions have been proposed on OWL and Description knowledge languages. The proposed extensions could be divided to the following three categories:
1. Probabilistic Extension
2. Possibilistic Extension
3. Fuzzy Extension
Among the three types of extensions we are going to focus on fuzzy extensions which has been the most active category among researchers [3, 4, 5, 6].There is a fundamental difference in the semantics of fuzzy logic and probabilistic logic. In fuzzy logic, a statement can be true to a certain extent or an entity belongs to a class to a certain degree. This degree is assumed to be known with certainty. In probabilistic reasoning, there is a probability that a statement is true or false, but the statement itself is either true or false, but neither both nor something in between. Hence fuzzy logic sees the world as continuous instead of binary, while probabilistic logics make a claim about the randomness of the world or the observer’s state of certainty [8].
2006: In 2006 Fuzzy OWL was proposed in the National Technical University of Athens [1]. Fuzzy OWL is capable of capturing and reasoning about knowledge using their reasoning platform, Fuzzy Reasoning Engine (FiRE). Fuzzy OWL represents fuzzy classes and properties. A fuzzy class is defined by a membership function that returns the membership degree between [0,1] for a given object. Fuzzy OWL uses crisp OWL’s Syntax for class and property axioms and definitions, and FiRE uses RACER DL [2] engine syntax.
2005: Semantic Web Rule Language (SWRL) is a proposal that combines OWL (DL and Lite) with the Rule Markup Language (RuleML). Fuzzy-SWRL (f-SWRL) is a fuzzy extension of Semantic Web Rule Language [7]. In both the antecedent and consequent of SWRL rules atoms can have weights between [0, 1]. f-SWRL provides a powerful and flexible knowledge representation and very convenient for multimedia as well as semantic web.
2.3 Application of Fuzzy in Semantic Web Systems
2007: A recommender system using temporal ontologies is proposed in [10]. The agents in the system provide preference and uncertain lists to the user. The uncertain list is the same type of information in the preference list but the acquired data is based on the known products in the agent’s ontology. The agent’s ontology contains the previous feedbacks about the products.
2005: Haibin and Yan proposed a framework called soft Semantic Web Services agent (soft SWS agent) [11] providing high quality semantic web services using fuzzy neural networks with genetic algorithms. The core of soft SWS agent is the intelligent inference engine (IIE) which uses a four layer architecture fuzzy neural network. Linguistic variables in layer one change to output variables in layer four after the fuzzy process in the layered architecture.
[12] introduces a concept-matching information retrieval system that is capable of “retrieving web pages that are conceptually related to the implicit concepts of the query”. The system uses Fuzzy interrelations and Synonymy-Based Concept Representation Model (FIS-CRM) to extract the concepts. The vectors in FIS-CRM are fuzzy values representing “concept” occurrence instead of term occurrence.
Reference
[1] Stoilos G., Simou N., Stamou G., Kollias S. Uncertainty and the Semantic Web, IEEE Intelligent Systems, 2006
[2] www.sts.tu-harburg.de/~r.f.moeller/racer
[3] P. Vojtas. Fuzzy logic programming. Fuzzy Sets and Systems, 124:361-370, 2001.
[4] R. Ebrahim. Fuzzy logic programming. Fuzzy Sets and Systems, 117:215- 230, 2001.
[5] Cristinel Mateis. Extending disjunctive logic programming by t-norms. In LPNMR '99: Proceedings of the 5th International Conference on Logic Programming and Nonmonotonic Reasoning, pages 290-304, London, UK, 1999. Springer-Verlag.
[6] C. V. Damasio, L. M. Pereira. Antitonic logic programs. In 6th International Conference on Logic Programming and Nonmonotonic Reasoning, 2001.
[7] Jeff Z. Pan, Giorgos Stamou, Vassilis Tzouvaras, and Ian Horrocks. f- SWRL: A Fuzzy Extension of SWRL. In Proc. of the International Conference on Artificial Neural Networks (ICANN 2005), Special section on "Intelligent multimedia and semantics", 2005.
[8] Christopher Thomas and Amit Sheth. On the Expressiveness of the Languages for the Semantic Web – Making a Case for ‘A Little More’
[9] Lotfi A. Zadeh, Toward a perception-based theory of probabilistic reasoning with imprecise probabilities, In Journal of Statistical Planning and Inference 105 (2002) 233-264
[10] Trust based Recommender System for the Semantic Web
[11] Wang h., Zhang Y. Extensible Soft Semantic Web Services Agent
[12] Garces J., Olivas P. J., Romero F.P. Concept-Matching IR System Versus Word-Matching Information Retrieval Systems: Considering Fuzzy Interrelations for Indexing Web Pages. Journal of the American society for information science and technology, 57 (4). Pp. 564-576, 2006
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