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Search Engines After The Semanatic Web
 

Search Engines After The Semanatic Web

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    Search Engines After The Semanatic Web Search Engines After The Semanatic Web Presentation Transcript

    • Search Engines After The Semantic Web
      Presented by Samar Hamed
      Damascus University
    • Agenda
      Basic Semantic Web Principles
      Falcons Semantic Search Engine
      Search Engine Giants Experience (Google, Yahoo, Microsoft)
      Kngine New Promising Search Engine
      Summary
      References
    • Web evolving
      AKA Web 3.0 , web of thing , web of data
      where data objects are linked to other data objects (similar to how web pages are linked today)
      Computers will be able to make use of data residing inside web pages 
    • Data Representation
      RDF (Resource Description Frame Work)
    • Vocabulary
      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
      • RDFS (the RDF Vocabulary Description Language, also
      known as RDF Schema)
      • OWL (the Web Ontology Language)
    • RDFs vs OWL
      while RDFs Is Light Weight Ontology OWL extends the
      expressivity of RDFS with additional modeling primitives, For
      example, OWL defines the primitives
      equivalentClass
      equivalentProperty,
      inverseOf allows the creator of a vocabulary to state that one property
      I s the inverse of another, for example
      prod:directedis the owl:inverseOftv:director.
      increase the interoperability of data sets modeled using different vocabularies
    • RDFa
      RDFa is a way to express RDF data within XHTML by reusing the existing human-readable data without repeating content
      <div typeof="foaf:Person" xmlns:foaf="http://xmlns.com/foaf/0.1/">
      <p property="foaf:name">
      Alice Birpemswick
      </p>
      <p>
      Email: <a rel="foaf:mbox”href="mailto:alice@example.com">alice@example.com</a> </p>
      <p>
      Phone: <a rel="foaf:phone" href="tel:+1-617-555-7332">+1 617.555.7332</a>
      </p>
      </div>
    • Agenda
      Basic Semantic Web Principles
      Falcons Semantic Search Engine
      Search Engine Giants experience (Google,Yahoo, Microsoft)
      Kgine New Promising Search Engine
      Summary
      References
    • Falcons Semantic Search Engine
      ObjectSearch
      ConceptSearch
      DocumentSearch
    • Falcons Object Search
      Karlsruhe
    • Falcons Object Search
      Knows Peter Mika
    • Falcons Object Search
      Peter Mika Jim Hendler
    • Object Indexing
      To build the inverted index, search engines build for every object Virtual Document contains its descriptions using :
      local names
      associated literals of SW objects
      textual descriptions of its neighboring resources
      Term1
      object4
      object2
      object1
      Term2
      object2
      Term3
      object4
      object3
    • Object Indexing
      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 .
      WWW2008, International , World , Wide , Web, Conference, Beijing
    • Weighting and Similarity
      Both virtual document and query are represented as term vector in term vector space,
      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 ,
      To calculate similarity between the object and query cosine measure is used,
      the result is ranked based on the combination of of their relevance to the query and their popularity, where:
      The relevance score is calculated based on the cosine similarity measure
      and The popularity score is evaluated according to the number of RDF documents that SW objects are used by.
    • Light Weight inference
      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
      Falcons index not only its explicitly specified classes but also their super classes
      Class 1
      object3
      object2
      object1
      Class2
      object2
      Class3
      object4
      object1
    • Light Weight inference
      The system will not recommend all the sub classes instead it use simple algorithm to determine which ones should be provided to user
      OrgnizedEvent
    • Agenda
      Basic Semantic Web Principles
      Falcons Semantic Search Engine
      Search Engine Giants Experience (Google, Yahoo, Microsoft)
      Kngine New Promising Search Engine
      Summary
      References
    • Google Rich snippet
      Webmasters can provide structured data by using RDFa to
      mark up their web pages
      Google crawls RDFa data describing people, products, businesses, organizations, reviews, recipes, and events
      The search result will look smarter and richer according to the kind of data described in the result
    • Yahoo Search Monkey
      SearchMonkey is a system aims to make information presentation more intelligent when it comes to search results, by crawelingRDFa Data,
      enabling the people who know each result best - the publishers- to define what should be presented and how,
      it differs form google rich snippet ,where the site owners can develop the way the result should be presented by themselves.
    • Google Question Answering
      What is birth date of Catherine Zeta-Jones.
       
    • Google Question Answering
      what is the name of Britney Spears’s mother
    • Schema.org:library of vocabularies
      Google, Microsoft, and Yahoo In early June 2011 announced schema.org, a new service intended to create and support a common vocabulary for structured data markup on web pages.
      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.
       Schema.org appears to be Linked Data Lite with extremely limited support for vocabularies available at chema.org/docs/full.html
         |       
    • Extending Schema.org
       one can always create new schemas that are not at all on schema.org, if the content of your domain is not covered by any of the schema.org types.
      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 schema.org vocabulary
      If you publish content of an unsupported type, you have these options:
      Use a less-specific markup type. For example, schema.org 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
      .
      If you are feeling ambitious, use the schema.org extension system to define a new type
    • Microdata Model
      Schema.org does not use RDF as a data model instead it uses very generic Microdata supported bye HTM5drived from RDF Schema
    • MicrodatavsRDFa
      Microdata audience
      RDFa is extensible and very expressive, but the substantial complexity of the language has contributed to slower adoption.
      Schema.org vocabularies are search engine oriented more than domain specific like RDF
      Microdata can be converted to RDFa
      There is Schema.RDFS.org a site which is a complementary effort by people from the Linked Data community to express the terms provided by the Schema.org Vocabularies in RDF
      tagging information, Web page owners could improve the position of their site in search results—an  important source of traffic.
    • MicrodatavsRDFa
      RDFa audience
      All of the capabilities promised by schema.org are already fully supported in a richer more scalable manner in the form of RDFa
      The entire Web community should decide which features should be supported – not just Microsoft or Google or Yahoo
      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
    • Agenda
      Basic Semantic Web Principles
      Falcons Semantic Search Engine
      Search Engine Giants Experience (Google, Yahoo, Microsoft)
      KngineNew Promising Search Engine
      Summary
      References
    • KngineNew Promising Search Engine
      Egyptian startup Kngine has announced that its new Kngine search engine has gone live in 2010.
      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
    • Smart Information
      Yes Man
    • Words with Multiple Meanings
      Java
    • Comparisons
      iPhonevsiPhone 3G iPhone 3GS
    • Answer your questions
      Who is the director of 2012
    • Updated Information
      (Weather, Stock, Currency Price, and Sport Matches Results)
      Latest world cup matches results
    • Agenda
      Basic Semantic Web Principles
      Falcons Semantic Search Engine
      Search Engine Giants Experience (Google, Yahoo, Microsoft)
      Kngine New Promising Search Engine
      References
    • Taha, E. Linked Data :State of The Art; Department of Software Engineering and Information System, 2010.
      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.
      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.
      Schema.org and the Semantic Web. prototypo.blogspot.com/2011/06/schemaorg-and-semantic-web.html (accessed June 3,2011).
      LUR, X. Kngine: The Smartest Search Engine Ever? http://www.techxav.com/2010/04/09/kngine-the-smartest-search-engine-ever (accessed APRIL 9, 2010).
      Shadbolt, N.; Hall, W.; Berners-Lee, T. The Semantic Web Revisited; IEEE Computer Society, 2006.
    • Thank You