1. The 20th International Conference on Software Engineering and Knowledge Engineering (SEKE2008) Department of Electrical and Computer Engineering http://www.ualberta.ca/~golmoham/ Koosha Golmohammadi

2. Fuzziness in the Semantic Web: Survey and Future Directions 2 of 13 SEKE2008 Extracting information from the web is not trivial due to: –exponential growth of the web contents –rapidly growing number of situations on the web that involve uncertainties or inconsistencies uncertainty imprecision Standard representation of uncertainty and imprecision in the web environment is highly desirable Introduction

3. Fuzziness in the Semantic Web: Survey and Future Directions 3 of 13 SEKE2008 Discuss web utilization situations that would benefit from the application of uncertainty and approximate reasoning Review methodologies that can be applied to these situations focusing on fuzzy approaches Highlight potentials for future research works that enable agents to provide high quality services in existence of imprecise information Objectives

4. Fuzziness in the Semantic Web: Survey and Future Directions 4 of 13 SEKE2008 The Semantic Web (the web evolution)

5. Fuzziness in the Semantic Web: Survey and Future Directions 5 of 13 SEKE2008 The Semantic Web (the web evolution) cont.

6. Fuzziness in the Semantic Web: Survey and Future Directions 6 of 13 SEKE2008 The Semantic Web is a “living organism” combining autonomously evolving data sources/knowledge repositories The Semantic Web - Web of Data SW promises: Define and link the unstructured data on the web in a way that enables machines for automation, integration and reuse of data across various applications Offer developers a framework to make intelligent decisions using logic rules Offer an environment in which agents are able to perform tasks on behalf of the user Integrate data on the web and create a web of data ultimately

7. Fuzziness in the Semantic Web: Survey and Future Directions 7 of 13 SEKE2008 Information correctness and availability Information imprecision Concept mapping between ontologies Identification and composition of the web services Example scenarios

8. Fuzziness in the Semantic Web: Survey and Future Directions 8 of 13 SEKE2008 Real world is informal and involves knowledge that is imprecise, uncertain, partially true and approximate –Answers from different sources come with different degrees of confidence (e.g. query systems) –Impossible to make boundaries for a lot of concepts (e.g. cheap room, close to downtown etc.) Fuzzy principles in the SW

9. Fuzziness in the Semantic Web: Survey and Future Directions 9 of 13 SEKE2008 SW knowledge representation using fuzzy methods Current Status Extensions to Ontology Web Language (OWL) –Fuzzy OWL: a class is defined by membership functions and the membership of each object is a fuzzy value –Fuzzy extension of SHOIN: subsumption relation between classes and the entailment relation is no more crisp Extensions to Semantic Web Rule Language (SWRL) –Fuzzy-SWRL: rules atoms can have weights in [0,1] Combination of fuzzy logic and Formal Concept Analysis –Fuzzy Ontology Generation frAmework (FOGA): is a framework to represent the uncertainty information by a fuzzy value

10. Fuzziness in the Semantic Web: Survey and Future Directions 10 of 13 SEKE2008 Future Directions Automatic construction of fuzzy ontologies (where relationships among concepts/properties are fuzzy membership degrees) and interaction with crisp ontologies Development of fuzzy-based methods and algorithms for matching and comparison of ontologies Integration of fuzzy methods and rough sets for representing ontologies to handle different facets of imperfect knowledge Development of reasoning systems for fuzzy DL and Fuzzy OWL-DL SW knowledge representation using fuzzy methods

11. Fuzziness in the Semantic Web: Survey and Future Directions 11 of 13 SEKE2008 Current Status Collaborative filtering multi-agent model: … Soft Semantic Web Services agents: provides high quality semantic web services using fuzzy neural networks and genetic algorithms Concept-matching information retrieval system: uses fuzzy synonymy and fuzzy generality to retrieve web pages that are conceptually related to the implicit concepts of the query Ambient Intelligent (AmI) systems: provide fuzzy web services - transform rough information on sensors, actuators and services towards “smart data” - using Fuzzy Markup Language Semantic Web search agent based on Fuzzy Conceptual Model: to handle the ambiguity and imprecision of the concept on the Internet The architecture that treats the trust as a degree that a source can be trusted: introduces a model that takes into account partial trust, distrust and ignorance simultaneously Semantic Web Services using fuzzy methods

12. Fuzziness in the Semantic Web: Survey and Future Directions 12 of 13 SEKE2008 Future Directions Developing technologies to support agents to use imprecise information and reason about it: –selection of most suitable services in the presence of partial information –integration of atomic services when they are not fully compatible –supporting user in human-centric multi-criteria decision making when multiple alternatives and service providers are available –Open-source tools for automatically identifying levels of information uncertainty and reason about that Semantic Web Services using fuzzy methods

13. Fuzziness in the Semantic Web: Survey and Future Directions 13 of 13 SEKE2008 Thanks and questions