Riding The Semantic Wave


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Riding The Semantic Wave

  1. 1. Riding Semantec WaveHi Folks! Are you all enjoying net-surfing ?Oh man! Dont ask me! I am sick of repeating my personal profile every time Iregister in any social site! Why cant it be tagged and semantic data be shared with allother sites ?Well its already happening .. lets go for a ride on the semantic wave (Web) !First get blessed by the God of Internet (Tim berners Lee) " So the Net and the Web may both be shaped as something mathematicians call a Graph, but they are at different levels. The Net links computers, the Web links documents. Now, people are making another mental move. There is realization now, "Its not the documents, it is the things they are about which are important"...... Biologists are interested in proteins, drugs, genes. Businesspeople are interested in customers, products, sales. We are all interested in friends, family, colleagues, and acquaintances. There is a lot of blogging about the strain, and total frustration that, while you have a set of friends, the Web is providing you with separate documents about your friends. One in facebook, one on linkedin, one in livejournal, one on advogato, and so on. The frustration that, when you join a photo site or a movie site or a travel site, you name it, you have to tell it who your friends are all over again. The separate Web sites, separate documents, are in fact about the same thing -- but the system doesnt know it." -- revolutionary idea of Giant Global Graph (GGG) (http://dig.csail.mit.edu/breadcrumbs/node/215 )This is how Tim in his inimitably simple language attempts to shape the fate ofthe Internet (his brain child) !So what all we need is a social graph reusing the same source of user data andmaintaining relationships between user documents intelligently. This global graph isalso referred as Semantic Web elevating the user experience one level up from Netand Web to GGG (Web 3.0).In a nutshell, it is not about revealing ones secure data to another rather allowing theuser be connected to the data from peer sites (routing the nodes social graph) .The graph is expressed in FOAF format. FOAF metadata can be interpreted by anyother device/application which is part of the graph (Photo-sharing, travel sites, )A public RDF URI of the FOAF document is exported for sharing across interestedparties.
  2. 2. Gone are the days of the XML parser and DOM tree! Semantic Web evolving aroundRDF parser creating RDF graph in memory.references :FOAF and OpenID: two great tastes that taste great together by Dan ConnollyWhitelisting blog post by Sean B. PalmerTim pointed the tip of the iceberg .. to a new horizon of internet ... opened thefloodgate of plethora of possibilities for SEMANTIC WEB !Well technically speaking Web 3.0 = Web 2.0 + Semantic WebThe software metadata build on the existing value provided by social networks,folksonomies, and collaborative filtering that are already on the Web.Read the story of Radar Network (next big thing after GOOGLE) by its creatorfascinating - Nova Spivak Consider this scenario: Say you want to arrange a dinner at an upcoming conference. Today you might go through your address book and ping folks by e-mail to see whos attending. Then you probably send out e-mail invitations to dinner. You go back and forth with the group on the place and time, somehow you all agree, and then somebody makes a reservation. Files fly back and forth, with humans at the center. In the semantic Web, your software agent will "know" in advance whats involved in arranging a dinner. Instead of you sending out a flurry of e- mails, the agent could cull the conference attendees and make a list of potential invitees. It might also look through your address book to see which of your friends live in the city where the conference is being held. Once a list of potential dinner guests has been approved by you, the agent would negotiate the date and time with everyone elses agents via a calendar database, pick a restaurant from another database based on availability and your personal preferences, make the reservation, and send out directions. In a GPS- enabled world, it could even let you know how far a guest who is running late has to go.There are many semantic magicians ! Radar, Garlik, Metaweb Technologies,Powerset, and ZoomInfo - just to name a few !Theories fast translating into practices in Semantic Web space ! Semantic Tech aimsat encapsulating business domain knowledge used by many applications. This meansthat Semantic applications are thin because they work with “smart” data. All thebusiness ruleslogic is held in the models shared across applications.
  3. 3. A Semantic Web application is based on an architecture of various layers :data capture and analysis, data merging, semantic modeling, display anddeployment each step following the standards of knwledge model (RDF, RDFS,OWL, SWRL, and SPARQL)Semantic models represent knowledge about the world in which the system operates.A semantic application uses knowledge models in its operation. Using the modelsintelligently or "reasoning over the model"encompasses a very simple process ofgraph search to intricate inferencing over the model.Lets understand the semantic buzzwords : ** Taxonomies are hierarchies that establish “parent-child” relationship between its concepts. ** Simple ontologies are just networks of connections; richer ontologies include rules and constraints governing these connections. ** Knowledge Models are different from Object Models : ** The Resource Description Framework (RDF) is a foundation for representing and processing metadata; it provides interoperability between applications that exchange machine readable information on the Web. RDF integrates a variety of applications from library catalogs and world-wide directories to syndication and aggregation of news, software, and content to personal collections of music, photos, and events using XML as interchange syntax. ** Web Ontology Language along with Resource Description Framework defines the Semantic Web. ** Social Neworking (who knows who) evolved into Semantic Network (who knows what). The idea is to build reasoning on a task - Taskonomy. ** The intelligent agent assembles the recommendations and reasoning references against the task/topic and presents to the users > pull the artefacts associated with tags > find similar questions - case based reasoning (who are all the ppl solved the same problem) > adds the new user as the context for the topic ** Semantic Graph containing nodes connecting human and resources (author + document). It is a directed graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between the concepts. ** A dictionary of words labeled with semantic classes so associations can be drawn between words that have not previously been encountered while building a knowledge base.
  4. 4. So what are the semantic possibilities ?reference : Ontology Modelling White Paper (TopQuadrant) (1) Navigational Search The idea is to use topical directories, or taxonomies, to help people narrow in on the general neighborhood of the information they seek. A Taxonomy includes user profiles, user goals and typical tasks performed is used to drive a search engine. Multiple interrelated taxonomies are used to optimize information accessed by different stakeholders. Taxonomies and ontologies are used to suggest related subjects. (2) Automated Content Tagger semantic tags can be generated to make a document be "well known" by external systems so that search, integratation or invocation of other applications becomes more effective. Tags are automatically inserted based on the computer analysis of the information, typically using natural language analysis techniques. A predefined taxonomy or ontology of terms and concepts is used to drive the analysis. (3) Topic-based Search To provide precise and concept-aware or task-oriented search capabilities specific to an area of interest using knowledge representations across multiple knowledge sources both structured and un-structured. Knowledge model provides a way to map translation of queries to knowledge resources. (4) Context-Aware Retriever To retrieve knowledge from one or more systems that is highly relevant to an immediate context, through an action taken within a specific setting -- typically in a user interface. A user no longer needs to leave the application they are in to find the right information. Knowledge model is used to represent context. This “profile” is then used to constrain a concept-based search. (5) Expert Locator To provide users with convenient access to experts in a given area who can help with problems, answer questions, locate and interpret specific documents, and collaborate on specific tasks. Knowing who is an expert in what can be difficult in an organization with a large workforce of experts. Expert Locator could also identify experts across organizational barriers.
  5. 5. The profiles of experts are expressed in a knowledge model. This can then be used to match concepts in queries to locate experts. (6) Navigational Search Use topical directories, or taxonomies, to help people narrow in on the general neighborhood of the information they seek.These are just few mind blogging techniques !Lets now see how the guru of intelligent machines materializing the dream ofUNIFIED WORLD DATABASE ! Here is the real Web 3.0 machine !reference : Newyork Times The idea of a centralized database storing all of the world’s digital information is a fundamental shift away from today’s World Wide Web, which is akin to a library of linked digital documents stored separately on millions of computers where search engines serve as the equivalent of a card catalog.... information is structured in such a way so that software programs can discern relationships and even meaning. For example, an entry for California’s governor, Arnold Schwarzenegger, would be entered as a semantic TOPIC that would have various attributes or semantic VIEWS describing him as an actor, politician and athlete — listing them in a meanigful structured way in the database I searched Unicorn and freebase organized the info in the most meaningful manner!reference : Tim OreillyOnce we really understand a bit about underlying methodology of METAWEB wecan realize how amazing it is ! Metaweb first swallows the contents of the webs freely accessible databases, including much of wikipedia, and song tracks from musicbrainz etc... using its high-power AI brain... It then turns its users loose on not just adding more data items but making connections between them by filling out meta tags that categorize or
  6. 6. otherwise connect the data items, using a typology that can be extended by users, wiki-style.Well now howbout building the family tree of the whole world ! Sounds crazy ? Whynot just get started @Geni . (http://www.geni.com/)Happy riding the Semantic Wave !