RDF for Life Sciences

Transcription

1 RDF for Life Sciences Presentation to Oracle Life Sciences User Group June 23, 2004 John Wilbanks World Wide Web Consortium (W3C)

2 What is the W3C? Founded in 1994 by Tim Berners-Lee Develops common protocols for the WWW that promote its evolution and ensure its interoperability 450 member organizations, 70 FTEs Primary offices in France, Japan, USA Key Goals: Universal Access, Interoperability, Evolvability, Decentralization, Trust, Multimedia, Semantic Web

3 The Semantic Web Vision When data of any sort are placed in storage, they are filed alphabetically or numerically, and information is found (when it is) by tracing it down from subclass to subclass...the human mind does not work that way. It operates by association. Vannevar Bush, As We May Think, 1945 I have a dream for the Web... and it has two parts. Tim Berners-Lee, Weaving the Web

4 Where is the SW Most Useful? Application domains with: Large, rapidly changing databases (both schema and data) Large, rapidly changing metadata Rich metadata Multiple linkages between internal databases Multiple linkages between internal system and external information sources

5 How Can SW Affect Life Sciences? Search for lymphocyte as a keyword doesn't represent the complexity...

6 What Is The Semantic Web? Extends the WWW: Information maintained in structured form Knowledge is expressed as descriptive statements, saying some relationship exists between one thing and another Pat knows Joe The Semantic Web is what we get when we perform the same globalization process to Knowledge Representation that the Web initially did to Hypertext

7 The Semantic Web Stack

8 What The SW Isn't Remove the centralized concepts: Absolute truth Total knowledge Total provability 404 not found - invaluable A Semantic Web is not Artificial Intelligence A Semantic Web is not a re-run of previous work (KIF, cyc)

9 Resource Description Framework RDF provides the basis for generic tools for authoring, manipulating, and searching machine understandable data on the Web RDF extends the Web into a machineprocessable repository of information Composed of subject, verb, object triples Each triple represents a complete fact Each triple can be joined with other triples, but retains its own unique meaning

10 RDF/XML: on demand data integration human hemoglo bin LSID human hemoglo bin LSID human hemoglo bin LSID derives from GenBank + atagccgta cctgcgagt ctagaagct is a oxygen transport protein Gene Ontology + has 3D structure PDB human hemoglo bin LSID derives from is a has 3D structure Unified view atagccgta cctgcgagt ctagaagct oxygen transport protein Source: Dennis Quan

11 How Hard Is It To Start? Like adding meta tags <rdf:rdf xmlns:rdf=" xmlns:dc=" <rdf:description rdf:about=" dc:creator="john Wilbanks" dc:title="my Oracle Talk" dc:description="john's ramblings about Semantic Web for Life Sciences" dc:date=" " /> </rdf:rdf> O'Reilly: Practical RDF by Shelley Powers

12 The Dublin Core and RDF

13 How Else Do I Get RDF? Database schema conversion Text mining Embedded in page User generated RDF Service provided by site Batch text parsing Metadata servers N3: Human Readable Notation

14 Networked Ontologies The SW

15 Who Is Building The SW? Early Adopters in life sciences Aventis Affymetrix Nature Publishing Group MyGrid S-Moby DAS Major IT Vendors Oracle Network Data Model IBM LSID, Haystack Sun - SwoRDFish Adobe XMP HP Jena W3C Advanced Development

16 Oracle Network Data Model Subject:Predicate:Object (RDF) becomes...node:link:node (NDM) Manage all RDF in central schema User level access functions Constructors to query and update RDF Stored in NDM as a logical network Power of Oracle DB + power of semantic context not just for research any more

17 Nature Publishing Group's Urchin General news feeds (BBC, NYT, Yahoo!, ) Scientific news feeds (NPG, Science Daily, Medscape, ) Local NPG content and databases Information scraped from selected web pages Urchin database Subject-specific news feeds Dynamic, personalised news pages Breaking news alerts

18 Nature Publishing Group's Copernicus Bibliographic information: NPG article DB, PubMed, etc. Semantic Web sources: NCI, FOAF files Data mining of legacy literature Info from authors: personal, citation, scientific (GenBank, SwissProt, PDB, NCI, PACS, ) Copernicus database Responses to specific queries: HTML, SVG, Passive alerting: collaborative filtering, Bayesian filtering, Web Services : database interoperability, Office 2003 integration, source: Nature Publishing Group

19 What Next? Application Development is the next step - browsers, search engines, query tools, crawlers etc. - first versions are global : jena2, haystack, cwm Not yet deployable Next step: solve real-world problems, and in doing so, harden and deploy the fundamentals of the semantic web Use Cases Needed! What good is a spec if it isn't built against applications?