Search Engines After The Semanatic Web


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Search Engines After The Semanatic Web

  1. 1. Search Engines After The Semantic Web<br />Presented by Samar Hamed<br />Damascus University<br />
  2. 2. Agenda<br />Basic Semantic Web Principles<br />Falcons Semantic Search Engine<br />Search Engine Giants Experience (Google, Yahoo, Microsoft)<br />Kngine New Promising Search Engine<br />Summary<br />References <br />
  3. 3. Web evolving <br />AKA Web 3.0 , web of thing , web of data<br />where data objects are linked to other data objects (similar to how web pages are linked today)<br />Computers will be able to make use of data residing inside web pages <br />
  4. 4. Data Representation <br />RDF (Resource Description Frame Work)<br />
  5. 5. Vocabulary<br />RDF provides a generic, abstract data model for describing resources using subject, predicate, object triples. However, it does not provide any domain-specific terms for describing classes of things in the world and how they relate to each other. This function is served <br /><ul><li>RDFS (the RDF Vocabulary Description Language, also </li></ul> known as RDF Schema) <br /><ul><li>OWL (the Web Ontology Language) </li></li></ul><li>RDFs vs OWL<br />while RDFs Is Light Weight Ontology OWL extends the <br /> expressivity of RDFS with additional modeling primitives, For <br /> example, OWL defines the primitives <br />equivalentClass<br />equivalentProperty,<br />inverseOf allows the creator of a vocabulary to state that one property<br /> I s the inverse of another, for example <br />prod:directedis the owl:inverseOftv:director.<br /> increase the interoperability of data sets modeled using different vocabularies <br />
  6. 6. RDFa<br />RDFa is a way to express RDF data within XHTML by reusing the existing human-readable data without repeating content <br /><div typeof="foaf:Person" xmlns:foaf=""> <br /><p property="foaf:name"><br /> Alice Birpemswick<br /> </p><br /><p><br />Email: <a rel="foaf:mbox”href=""></a> </p><br /> <p> <br />Phone: <a rel="foaf:phone" href="tel:+1-617-555-7332">+1 617.555.7332</a> <br /></p><br /> </div><br />
  7. 7. Agenda<br />Basic Semantic Web Principles<br />Falcons Semantic Search Engine<br />Search Engine Giants experience (Google,Yahoo, Microsoft)<br />Kgine New Promising Search Engine<br />Summary<br />References<br />
  8. 8. Falcons Semantic Search Engine<br />ObjectSearch<br />ConceptSearch<br />DocumentSearch<br />
  9. 9. Falcons Object Search<br />Karlsruhe<br />
  10. 10. Falcons Object Search<br />Knows Peter Mika<br />
  11. 11. Falcons Object Search<br />Peter Mika Jim Hendler<br />
  12. 12. Object Indexing<br />To build the inverted index, search engines build for every object Virtual Document contains its descriptions using :<br />local names <br />associated literals of SW objects<br />textual descriptions of its neighboring resources <br />Term1<br />object4<br /> object2<br /> object1<br />Term2<br /> object2<br />Term3 <br /> object4<br /> object3<br />
  13. 13. Object Indexing<br />Falcons approach is to collect neighbors for a SW object starting from it, traversing the graph, and stopping until reaching URIs or literals but not blank nodes cause no terms can be collected from them .<br />WWW2008, International , World , Wide , Web, Conference, Beijing<br />
  14. 14. Weighting and Similarity<br />Both virtual document and query are represented as term vector in term vector space, <br /> The terms of the virtual document are weighted where term in the local name and labels are assigned a higher weighting coefficient than those in literal properties and neighbor's properties term , <br />To calculate similarity between the object and query cosine measure is used, <br />the result is ranked based on the combination of of their relevance to the query and their popularity, where:<br /> The relevance score is calculated based on the cosine similarity measure <br />and The popularity score is evaluated according to the number of RDF documents that SW objects are used by. <br />
  15. 15. Light Weight inference <br />Falcons index the classes of SW objects and provide a user-friendly navigation hierarchy of classes for users to refine the search results using class-inclusion reasoning to discover implicit types of objects<br />Falcons index not only its explicitly specified classes but also their super classes <br />Class 1<br />object3<br />object2<br /> object1<br />Class2<br /> object2<br />Class3<br /> object4<br /> object1<br />
  16. 16. Light Weight inference <br />The system will not recommend all the sub classes instead it use simple algorithm to determine which ones should be provided to user<br />OrgnizedEvent<br />
  17. 17. Agenda<br />Basic Semantic Web Principles<br />Falcons Semantic Search Engine<br />Search Engine Giants Experience (Google, Yahoo, Microsoft)<br />Kngine New Promising Search Engine<br />Summary<br />References<br />
  18. 18. Google Rich snippet<br />Webmasters can provide structured data by using RDFa to <br /> mark up their web pages <br />Google crawls RDFa data describing people, products, businesses, organizations, reviews, recipes, and events<br /> The search result will look smarter and richer according to the kind of data described in the result <br />
  19. 19. Yahoo Search Monkey<br />SearchMonkey is a system aims to make information presentation more intelligent when it comes to search results, by crawelingRDFa Data,<br />enabling the people who know each result best - the publishers- to define what should be presented and how,<br />it differs form google rich snippet ,where the site owners can develop the way the result should be presented by themselves. <br />
  20. 20. Google Question Answering<br />What is birth date of Catherine Zeta-Jones.<br /> <br />
  21. 21. Google Question Answering<br />what is the name of Britney Spears’s mother<br />
  22. 22. of vocabularies <br />Google, Microsoft, and Yahoo In early June 2011 announced, a new service intended to create and support a common vocabulary for structured data markup on web pages.<br />The idea is to provide a library of vocabularies to embed machine-readable data into web pages in a manner that can be fully exploited across search engines.<br /> appears to be Linked Data Lite with extremely limited support for vocabularies available at<br />   |       <br />
  23. 23. Extending<br /> one can always create new schemas that are not at all on, if the content of your domain is not covered by any of the types.<br /> If the schema gains search engines may start using this data.) Extensions that gain significant adoption on the web may be moved into the core vocabulary<br />If you publish content of an unsupported type, you have these options:<br />Use a less-specific markup type. For example, has no "Professor" type. However, if you have a directory of professors in your university department, you could use the "person" type to mark up the information for every professor in the directory<br />.<br />If you are feeling ambitious, use the extension system to define a new type<br />
  24. 24. Microdata Model<br /> does not use RDF as a data model instead it uses very generic Microdata supported bye HTM5drived from RDF Schema <br />
  25. 25. MicrodatavsRDFa<br />Microdata audience <br />RDFa is extensible and very expressive, but the substantial complexity of the language has contributed to slower adoption. <br /> vocabularies are search engine oriented more than domain specific like RDF<br />Microdata can be converted to RDFa<br />There is a site which is a complementary effort by people from the Linked Data community to express the terms provided by the Vocabularies in RDF <br />tagging information, Web page owners could improve the position of their site in search results—an  important source of traffic.<br />
  26. 26. MicrodatavsRDFa<br />RDFa audience <br />All of the capabilities promised by are already fully supported in a richer more scalable manner in the form of RDFa<br />The entire Web community should decide which features should be supported – not just Microsoft or Google or Yahoo<br />Google and Yahoo already support Microdata and RDFa in their advanced search services (Google Rich Snippets and Yahoo Search). So, why is it that we cannot continue to use <br />
  27. 27. Agenda<br />Basic Semantic Web Principles<br />Falcons Semantic Search Engine<br />Search Engine Giants Experience (Google, Yahoo, Microsoft)<br />KngineNew Promising Search Engine<br />Summary<br />References<br />
  28. 28. KngineNew Promising Search Engine<br />Egyptian startup Kngine has announced that its new Kngine search engine has gone live in 2010. <br />Most existing semantic search they draw their results from a limited number of sites such as Wikipedia and Freebase. Kngine, however, has expanded beyond those sources, and seeks to index structures information <br />
  29. 29. Smart Information<br />Yes Man<br />
  30. 30. Words with Multiple Meanings<br />Java<br />
  31. 31. Comparisons<br />iPhonevsiPhone 3G iPhone 3GS<br />
  32. 32. Answer your questions<br />Who is the director of 2012<br />
  33. 33. Updated Information<br />(Weather, Stock, Currency Price, and Sport Matches Results)<br />Latest world cup matches results<br />
  34. 34. Agenda<br />Basic Semantic Web Principles<br />Falcons Semantic Search Engine<br />Search Engine Giants Experience (Google, Yahoo, Microsoft)<br />Kngine New Promising Search Engine<br />References<br />
  35. 35. Taha, E. Linked Data :State of The Art; Department of Software Engineering and Information System, 2010. <br />Heath, T.; Bizer, C. Linked Data: Evolving the Web into a Global Data Space :Synthesis Lectures on the Semantic Web: Theory and Technology, 1st ed.; Morgan & Claypool, 2011. <br />Cheng, G.; Qu, Y. Integrating Lightweight Reasoning into Class-Based Query Refinement for Object Search; Scientific papaer; Institute of Web Science, School of Computer Science and Engineering,Southeast University: Nanjing, 2008. <br /> and the Semantic Web. (accessed June 3,2011). <br />LUR, X. Kngine: The Smartest Search Engine Ever? (accessed APRIL 9, 2010). <br />Shadbolt, N.; Hall, W.; Berners-Lee, T. The Semantic Web Revisited; IEEE Computer Society, 2006. <br />
  36. 36. Thank You<br />