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Majid Sazvar sazvar@stu-mail.um.ac.ir Knowledge Engineering Research Group

Majid Sazvar sazvar@stu-mail.um.ac.ir Knowledge Engineering Research Group Ferdowsi University of Mashhad. Semantic Web Reasoning. Overview. Introduction What is the meaning? Where is meaning in the Semantic Web? Structured Web vs. Reasonable Web SWR Challenges and Solutions Conclusion.

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Majid Sazvar sazvar@stu-mail.um.ac.ir Knowledge Engineering Research Group

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  1. Majid Sazvar sazvar@stu-mail.um.ac.ir Knowledge Engineering Research Group Ferdowsi University of Mashhad Semantic Web Reasoning

  2. Overview Introduction What is the meaning? Where is meaning in the Semantic Web? Structured Web vs. Reasonable Web SWR Challenges and Solutions Conclusion 2 Semantic Web Reasoning - Majid Sazvar - 2011

  3. The WWW is penetrating our society Social contacts (social networking platforms, blogging, ...) Economics (buying, selling, advertising, ...) Administration (eGovernment) Education (eLearning, Web as information system, ...) Work life (information gathering and sharing) Recreation (games, role play, ...) Introduction 3 Semantic Web Reasoning - Majid Sazvar - 2011

  4. The current Web Immensely successful. Huge amounts of data. Syntax standards for transfer of unstructured data. Machine-processable, human-readable documents. BUT: Content/knowledge cannot be accessed by machines. Meaning (semantics) of transferred data is not accessible. Introduction 4 Semantic Web Reasoning - Majid Sazvar - 2011

  5. Limitations of the current Web Too much information with too little structure and made for human consumption Content search is very simplistic future requires better methods Web content is heterogeneous in terms of content in terms of structure in terms of character encoding future requires intelligent information integration Humans can derive new (implicit) information from given pieces of information but on the current Web we can only deal with syntax requires automated reasoning techniques Introduction 5 Semantic Web Reasoning - Majid Sazvar - 2011

  6. Basic ingredients for the Semantic Web Open Standards for describing information on the Web Methods for obtaining further information from such descriptions Introduction 6 Semantic Web Reasoning - Majid Sazvar - 2011

  7. The Semantic Web Layer Cake Introduction 7 Semantic Web Reasoning - Majid Sazvar - 2011

  8. Syntax and Semantics Syntax: character strings without meaning Semantics: meaning of the character strings What is the meaning? 8 Semantic Web Reasoning - Majid Sazvar - 2011

  9. Semantics of Programming Languages What is the meaning? 9 Semantic Web Reasoning - Majid Sazvar - 2011

  10. Semantics of Logic What is the meaning? 10 Semantic Web Reasoning - Majid Sazvar - 2011

  11. What Semantics Is Good For Semantic Web requires a shareable, declarative and computable semantics. I.e., the semantics must be a formal entity which is clearly defined and automatically computable. Ontology languages provide this by means of their formal semantics. Semantic Web Semantics is given by a relation – the logical consequence relation. What is the meaning? 11 Semantic Web Reasoning - Majid Sazvar - 2011

  12. In other words We capture the meaning of information not by specifying its meaning (which is impossible) but by specifying how information interacts with other information. We describe the meaning indirectly through its effects. What is the meaning? 12 Semantic Web Reasoning - Majid Sazvar - 2011

  13. Ontology languages Of central importance for the realisation of Semantic Technologies are suitable representation languages. Meaning (semantics) provided via logic and deduction algorithms (automated reasoning). Scalability is a challenge. Where is meaning in the Semantic Web? 13 Semantic Web Reasoning - Majid Sazvar - 2011

  14. Explicit vs. Implicit Knowledge if an RDFS document contains and then is implicitly also the case: it’s a logical consequence. (We can also say it is deduced (deduction) or inferred (inference). We do not have to state this explicitly. Which statements are logical consequences is governed by the formal semantics (covered in the next session). Where is meaning in the Semantic Web? 14 Semantic Web Reasoning - Majid Sazvar - 2011

  15. Where is meaning in the Semantic Web? 15 Semantic Web Reasoning - MajidSazvar - 2011 RDFS Semantic

  16. Where is meaning in the Semantic Web? 16 Semantic Web Reasoning - MajidSazvar - 2011 OWL-Horst Semantic

  17. Where is meaning in the Semantic Web? 17 Semantic Web Reasoning - MajidSazvar - 2011 OWL-Horst Semantic

  18. Using implicit knowledge Backward Chaining Method (Goal-Driven) Where is meaning in the Semantic Web? 18 Semantic Web Reasoning - Majid Sazvar - 2011

  19. Using implicit knowledge Forward Chaining Method (Data-Driven) Where is meaning in the Semantic Web? 19 Semantic Web Reasoning - MajidSazvar - 2011

  20. Structured Web vs. Reasonable Web 20 Semantic Web Reasoning - MajidSazvar - 2011 Importance of meaning in the Semantic Web • Structured Web • + • Semantic • = • Reasonable Web • = • Semantic Web

  21. Structured Web vs. Reasonable Web 21 Semantic Web Reasoning - MajidSazvar - 2011 LOD Cloud 19,562,409,691 Triples* * http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/DataSets/Statistics The Semantic Web is Here But we're not ready!?

  22. Vastness The World Wide Web contains at least 24 billion pages as of this writing (June 13, 2010). Any automated reasoning system will have to deal with truly huge inputs. Distributed Reasoning is a promising technique. SWR Challenges and Solutions 22 Semantic Web Reasoning - Majid Sazvar - 2011

  23. Vagueness These are imprecise concepts like "young" or "tall". This arises from the vagueness of user queries, of concepts represented by content providers, of matching query terms to provider terms and of trying to combine different knowledge bases with overlapping but subtly different concepts. Fuzzy logic is the most common technique for dealing with vagueness. SWR Challenges and Solutions 23 Semantic Web Reasoning - Majid Sazvar - 2011

  24. Uncertainty These are precise concepts with uncertain values. For example, a patient might present a set of symptoms which correspond to a number of different distinct diagnoses each with a different probability. Probabilistic reasoning techniques are generally employed to address uncertainty. SWR Challenges and Solutions 24 Semantic Web Reasoning - Majid Sazvar - 2011

  25. Inconsistency These are logical contradictions which will inevitably arise during the development of large ontologies, and when ontologies from separate sources are combined. Deductive reasoning fails catastrophically when faced with inconsistency, because "anything follows from a contradiction". Inductive reasoning, defeasible reasoning and paraconsistent reasoning are techniques which can be employed to deal with inconsistency. SWR Challenges and Solutions 25 Semantic Web Reasoning - Majid Sazvar - 2011

  26. Privacy Q: Can a reasoner answer queries using hidden knowledge without exposing hidden knowledge? A: Privacy-Preserving Reasoning SWR Challenges and Solutions 26 Semantic Web Reasoning - Majid Sazvar - 2011

  27. Deceit This is when the producer of the information is intentionally misleading the consumer of the information. Trust and Cryptography techniques are currently utilized to alleviate this threat. SWR Challenges and Solutions 27 Semantic Web Reasoning - Majid Sazvar - 2011

  28. Conclusion We need to move towards the Reasonable Web. 28 Semantic Web Reasoning - Majid Sazvar - 2011

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