Semantic Web COMS 6135 Class Presentation Jian Pan Department of Computer Science Columbia University Web Enhanced Information Management

Semantic Web  An extension to the World Wide Web to make the web readable by machines, computers or software agents.  Goal achieved by adding metadata into web pages to describe data or info about the page, thus making the pages “ machine understandable ”.  Not necessarily will it make computers fully intelligent or self- aware, but it will make machine more able to find, integrate information, and also make inference.  It derived from Tim Berners-Lee ’ s vision of the web as a universal medium for data, information, and knowledge exchange.

Motivation of Semantic Web  Data in daily application are scattered in different formats computers can ’ t understand or utilize. Spreadsheets, Data in a relational DB, Data on your own desktop  Information on the web today are represented in natural language computers can ’ t understand.  Many tasks today require to combine data of different format on the Web Hotel and car rental info may come from different sites  We can use machine to integrate, create association on data, make inference on data if we can represent data in a machine understandable language: Metadata: visible to machines, invisible to human

An Example Application: Buying a DVD online Web Site MetaData Machine Agent Automatically process and make inference on data Web Site1 Traditional WebSemantic Web Site MetaData Site MetaData Search Final Result

Components: XML  XML: compliments HTML by adding tags that can describe the data. Machine codes such as Web Crawler thus can understand the data.

Components: RDF  RDF: Resource Description Framework: identifies a resource with its location and relations to other resources on the Web.  Everything on the Web is a resource, and can be described in RDF terms

Components: RDF(1)  Example RDF snippets <rdf:type rdf:BlockbusterMovie rdf:resource= " /rdf-schema#BlockbusterMovie"/> <rdf:type rdf:Movie rdf:resource= " /rdf-schema#Movie"/>

Components: RDF(2)  RDF have three components: Subject, Property, Value SubjectValue Property Spiderman 3 Is a kind of Blockbuster movie

Components: RDF(3)  A network of resources described by RDF can form a resource network

Components: RDF(4)  Semantic Web will enable software agents to make inference on resources described using RDF Spiderman 3 Blockbuster Movie rdf:type rdf:IsAClassOf

Components: URI  URI (Unified Resource Identifiers): the computer needs to be directed to where the resource is located  Every resource can be described by a URI.  URI not URL, doesn ’ t have to be a Web link, can be the computer on your refrigerator !

Components: URI(2) <rdf:type rdf:BlockbusterMovie rdf:resource= " /rdf-schema#BlockbusterMovie"/> <rdf:type rdf:Movie rdf:resource= " /rdf-schema#Movie"/> URI

To Enable Further Inference Capabilities  We need to describe the data in more detail:  Ontology : a vocabulary that tries to describe a resource and how their relationship with each other.  Human understand words, but computer need a dictionary to look up what the words mean, how they connect to each other to build its logic connections.

Components: Ontology Spiderman3 Tobey MaguireKirsten Dunst BlockBuster Movie Stars in Is a kind of Stars in

Example Revisited: Buying a DVD online  The entire workflow of buying a DVD online in a semantic web would be as follows:  The websites adds metadata to each DVD item to be readable by computers or software agents  The metadata would be in XML formatted RDF tags. RDF will describe in full all properties of the DVD item  There will be a coherent industry ontology created to describe each DVD item shared by all online DVD stores  Software agents on the client side traverse all DVD item metadata from different stores and return the desired search results to the user  The results returned by the agent will improve by inference what the user intend to search for.

Typical Applications  Symptom diagnosis: doctors informed of the patient ’ s symptoms will not always be able to determine the disease. Semantic Web using inference based on other online resources will make better suggestions for the doctor to choose  Biomedical applications: data mining to build connections between certain genes or protein structure of medicine with clinical trial results by using inference.

ChallengesChallenges  Many ontology are very difficult to create: use modularization (create only part of the ontology)  Debate of whether using rule based inference is better or using ontology based reasoning is better  Trust: can we trust the metadata provided? Who ’ is authorized to publish what metadata?  Improve the efficiency of the inference algorithms  Governance: no one can monitor all the rules, data in the Semantic Web

Thank You !

ReferencesReferences  Semantic Web - XML2000 by Tim Berners-Lee,   Introduction to the Semantic Web, Ivan Herman, International Conference on Semantic Web & Digital Libraries, 2007/02/21, Bangalore, India.  Semantic Web: Building on What Exists, 2006/04/04,Tim Berners-Lee, MIT Information Technology Conference keynote