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Semantic Web and Knowledge Representation

Semantic Web and Knowledge Representation. Sharath Srinivas CMSC 818Z, Spring 2007. Outline. Motivation Introduction Information centric perspective of semantic web Architecture of the Semantic Web Future Video and examples!. Motivation.

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Semantic Web and Knowledge Representation

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  1. Semantic Web and Knowledge Representation Sharath Srinivas CMSC 818Z, Spring 2007 Department of Computer Science, University of Maryland, College Park

  2. Outline • Motivation • Introduction • Information centric perspective of semantic web • Architecture of the Semantic Web • Future • Video and examples!

  3. Motivation • Is there any such task that a computer can do, which a human cannot do? … • 5 possible answers: • Yes, of course! • Not at all • Sort of, but most tasks that humans do cannot be done by computers. • Sort of, but most tasks that humans do can be done by computers • No Comments! • Why is it so? This is the state of affairs today All Computers do is what they are programmed to do!

  4. Motivation… • So, are computers dumb? • Yes…sort of! • Then why are we (Computer Scientists) spending our life on something that’s dumb? • To make them less dumb!!!

  5. Introduction • The Web is considered to be the most powerful information tool in history. • One of the most difficult resources to search and evaluate • “The ultimate goal of the  Web will be achieved when search engines can find the answer to the question of Life, the Universe and Everything else - obviously that will occur in Web 42.0” –Prof. Jim Hendler, MIND lab

  6. Introduction • Web 42.0 ??? • What is “Web 42.0”? • What is the current version of the web? • I decided to search for this on google… • No useful results • So I decided to post this question on a forum where people discuss stuff like this…

  7. Response…

  8. Intelligent Search • So, we need more intelligent search engines, that can understand the users • Google Answers example: • searching for words isn’t really what you want to do. You’d like to search for ideas, for concepts, for solutions, for answers… • Current information representation and retrieval techniques are not capable of achieving this.

  9. Need of the hour? • We need more intelligent Systems that can retrieve quality information. • For this we need better representation techniques of information. • Information is not data, it is knowledge derived from data.

  10. Information Dynamics ? Information Information Loss During transformation into its Representation Representation Representation

  11. Information Dynamics Information Dynamics… • Ideal Scenario Information Information Representation Representation Will this ever be possible?

  12. Semantic • semantic, a. and n. • a. Relating to signification or meaning.

  13. Semantic… • Making web pages machine readable • Combining information from multiple sources • Making inferences to find new knowledge

  14. My Web Page Advisor 2’s web Page Advisor 1’s web Page Semantic Web… My Web Page (which is a autonomous intelligent agent) should determine whom I should meet and at what time.

  15. Wedding Cake!

  16. Pieces of the cake… • Parts of the Semantic Web: • A Global naming schema (URI) • A standard syntax for describing data (RDF) • A syntax for representing the properties of the data (RDF Schema) • A standard means of describing the relationships between data (OWL)

  17. XML: User definable and domain specific markup • HTML: <H1>Introduction to AI</H1><UL> <LI>Teacher: Frank van Harmelen<LI>Students: 1AI, 1I<LI>Requirements: none</UL> XML: <course><title>Introduction to AI</title><teacher>Frank van Harmelen</teacher><students>1AI, 1I</students><req>none</req></course>

  18. XML document= labelled trees <course date=“...”><title>...</title><teacher>...</teacher> <name>...</name> <http>...</http><students>...</students></course>

  19. Syntax versus Semantics • Syntax: the structure of your data • Semantics: the meaning of your data • Two conditions necessary for interoperability: • Adopt a common syntax: this enables applications to parse the data. • Adopt a means for understanding the semantics: this enables applications to use the data.

  20. RDF

  21. RDF…

  22. RDF…combining Information

  23. RDF…combining Information • RDF…combining Information

  24. RDF…combining Information

  25. RDF…combining Information

  26. Wedding cake…

  27. RDF SChema

  28. Wedding cake…

  29. Ontology • Ontology • “... a specification of a conceptualisation.” • Vocabulary and relationships • RDFS • Classes and subclass relationships • Properties and subproperty relationships • Range and domain of properties

  30. Ontology…example Person subClassOf subClassOf range domain Student Researcher hasSuperVisor type type Frank Jeen hasSuperVisor

  31. Ontology • Identity (owl:sameAs) • Disjunction • something can be in one or other class but not both • Number restrictions • at least n of some property • no more than n of some property • Flavours: OWLLite, OWLDL,OWLFull

  32. What you can do • Mark up web pages • Present databases as RDF • Use and develop new ontologies

  33. Wedding cake…

  34. Logic, Proof and Reasoning

  35. Wedding cake…Revisited!! Proof, Logic and reasoning are active areas of research

  36. Trust • Self Intelligent agents: Can we trust them? Should I trust my agent? Don’t drive! Weather is bad

  37. Conclusion • Semantic web is no hype • Its already a reality • It is and it will continue to make Computers less dumb!

  38. References and Resources • MindLabs and Mindswap: Google it! • Wikipedia: Google Search: Semantic web Wiki • The talk given by Hugo Mills at the Hampshire Linux Users group: Cannot find using google… • www.hantslug.org.uk/cgi-bin/wiki.pl?TechTalks/3rdJune2006

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