3. 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
6. 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>
12. 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
13. 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
14. 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.
15. 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
16. 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
18. 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
19. 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.
22. 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 |
23. 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
24. Microdata Model Schema.org does not use RDF as a data model instead it uses very generic Microdata supported bye HTM5drived from RDF Schema
25. 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.
26. 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
28. 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
35. 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.
