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Evolution Towards Web 3.0: The Semantic Web

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This was a lecture I presented at Professor Stuart Madnick's class, "Evolution Towards Web 3.0" at the MIT Sloan School of Management on April 21, 2011. Please follow along with the speaker notes which add significant commentary to the slides.

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Evolution Towards Web 3.0: The Semantic Web

  1. Evolution Towards Web 3.0: <br />The Semantic Web<br />Experiences and Challenges on the Web and Inside Enterprises<br />Lee Feigenbaum<br />VP Technology & Client Services, Cambridge Semantics<br />Co-chair W3C SPARQL Working Group<br /><br />for “Evolution Towards Web 3.0”, April 21, 2011<br />
  2. Agenda<br />How did we get here?<br />Semantic Web: What and why<br />How is it used today?<br />Semantic Web challenges<br />
  3. Acknowledgement<br />Much material used gratefully with permission of Tim Berners-Lee. All opinions and conclusions are Lee Feigenbaum’s.<br />
  4. Web Evolution<br />1992<br />1993<br />1994<br />Widespread success of Web 1.0<br /><br /><br /><br /><br />Universality: anything can link to anything<br />Push information to users<br />Debut of Mosaic browser<br />1st image on the Web<br />
  5. Web Evolution<br />1994<br />1999<br />2004<br />2006<br />Web 1.0 is “here”.<br />IE7 has 1st complete AJAX stack<br />First Web 2.0 Conference<br />Highlights User-Generated<br /> Content<br />
  6. Building Silos<br />Web 1.0: The silo is the document<br />
  7. Building Silos<br />Web 2.0: The silo is the application<br />Image originally from March 2008 issue of The Economist and used with permission of creator David Simonds<br />
  8. Penetrating Silos: Building the Data Web<br />
  9. Penetrating Silos: Building the Data Web<br />
  10. Penetrating Silos: Building the Data Web<br />
  11. Penetrating Silos: Building the Data Web<br />
  12. Penetrating Silos: Building the Data Web<br />
  13. Penetrating Silos: Building the Data Web<br />
  14. Penetrating Silos: Building the Data Web<br />
  15. Web Evolution<br />1994<br />2004<br />2001<br />2007<br />2009<br />Web 1.0 is “here”.<br />Web 2.0 is “here”.<br />Semantic Web consumers include Google & Yahoo!<br />Semantic Web publishers include Best Buy, NY Times, US and UK gov’ts<br />
  16. Web Evolution<br />2001<br />2004<br />2008<br />2011<br />2007<br />1999<br />RIF<br />16<br />
  17. “The Semantic Web”<br />Link explicit data on the World Wide Web in a machine-readable fashion<br />…government data<br />…commercial data<br />…social data<br />In order to enable…<br />…targeted, semantic search<br />…data browsing<br />…automated agents<br />Semantic Web – 1st view<br />World Wide Web : Web pages :: The Semantic Web : Data<br />
  18. “Semantic Web technologies”<br />A family of technology standards that ‘play nice together’, including:<br />Flexible data model<br />Expressive ontology language<br />Distributed query language<br />Drive Web sites, enterprise applications<br />Data integration<br />Business intelligence<br />Large knowledgebases<br />…<br />Semantic Web – 2nd view<br />The technologies enable us to build applications and solutions that were not possible, practical, or feasible traditionally.<br />
  19. Names<br />
  20. Semantic Web<br />Web of Data<br />Giant Global Graph<br />Data Web<br />Web 3.0<br />Linked Data Web<br />Semantic Data Web<br />Enterprise Information Web<br />Branding<br />
  21. Value propositions<br />On the Web, the Semantic Web is about moving from linking documents to linking data<br />What’s the value proposition within the enterprise?<br />
  22. Evolution to Semantic Web Inside Enterprises<br />Relational TechnologySemantic Technology<br />Cathy<br />purchased<br />iPad<br />Based on tables<br /><ul><li>Rigid table stores only the things they’re designed to store
  23. Meaning (e.g. relationships) must come from the user or be built into software</li></ul>Based on a Web of data<br /><ul><li>Can accommodate new data as it arrives
  24. Understandable by human beings & machines
  25. Complements & builds upon traditional IT</li></li></ul><li>The Semantic Web Paradigm<br />
  26. Semantic Web Paradigm: Coping with Change<br />The World Changes<br />Traditionally:<br />Change is costly<br />Semantics:<br />Change is cheap<br />RDB 1<br />RDB 2<br />
  27. Integrated <br />Enterprise <br />Data<br />Data Silos(structured, semi-structured, unstructured data)<br />Excel<br />Email<br />MySQL<br />Sybase<br />Oracle<br />…At and Beyond Enterprise Scale<br />
  28. Semantics Puts Data Within Reach of Domain Experts<br />
  29. How is Semantic Web used today?<br />
  30. We’re not here yet.<br />Image from Trey Ideker via Enoch Huang<br />
  31. What is here today?<br />Do you use Web 3.0 in your day-to-day life?<br />
  32. The Linked Data Web, May 2007<br />
  33. The Linked Data Web, March 2008<br />May 12, 2009<br />31<br />
  34. The Linked Data Web, March 2009<br />32<br />
  35. The Linked Data Web, September 2010<br />
  36. Semantic Web In Use: Social Data<br />People, relationships<br />Friend Of A Friend (“FOAF”) – foaf:knows<br />Self-published or site-published (LiveJournal, hi5, …)<br />Blogs, discussion forums, mailing lists<br />Semantically Interlinked Online Communities (“SIOC”)<br />Plug-ins for popular blogging & CMS platforms<br />Calendars, vCards, reviews, … <br />One-offs<br />Why don’t we have portable social networks? Yet?<br />
  37. Social Data Example<br />Facebook Open Graph Protocol<br />
  38. Semantic Web In Use: Scientific Data<br />May 12, 2009<br />36<br />
  39. Example: Alzheimer’s Drug Discovery<br />What genes are involved in signal transduction and are related to pyramidal neurons?<br />
  40. General search: 223,000 hits, 0 results<br />
  41. Domain-limited search: Still 2,580 potential results<br />
  42. Specific databases: Too many silos!<br />
  43. Linked Scientific Data: 32 targeted results<br />
  44. Semantic Web In Use: Enterprises on the Web<br />Thesis: Describe your business more precisely and drive more (and better) traffic to your site<br />Example: NYTimes publishes their article classification scheme as linked data<br />Example: Best Buy, use RDFa to annotate product listings<br />
  45. Measurable Results<br />30% increase in search-engine traffic<br />15% increase in click-through-rate for search ads<br />
  46. Many and Varied Applications Across Industries<br />Health care and pharma<br />integration, classification, ontologies<br />Oil & Gas<br />integration, classification<br />Finance <br />structured data, ontologies, XBRL<br />Publishing <br />metadata<br />Libraries & museums <br />metadata, classification<br />IT <br />rapid application development & evolution<br />Semantic Web In Use: Inside the Enterprise<br />
  47. Targeting High-Potential Opportunities in Pharma<br />. . .<br />Profile<br />Territory<br />Preferred<br />targets<br />Regional<br />Analyst<br />Per-analyst<br />relevance filter<br />Universe of considered opportunities<br />High-potential<br />opportunities<br />Mobile device<br />
  48. Delivering Dynamic, Data-driven Websites<br />“<br />The development of this new high-performance dynamic semantic publishing stack is a great innovation for the BBC as we are the first to use this technology on such a high-profile site. It also puts us at the cutting edge of development for the next phase of the Internet, Web 3.0.<br />
  49. Semantic Web In Use: Government data<br />Since January 2010, 2,500 (large) datasets published as Linked Data<br />Since May 2009, 250,000 (smaller) datasets published (CSV, XML, …)<br />RPI project to convert datasets toLinked Data<br />
  50. Tim Berners-Lee @ TED2010<br /><br />
  51. Semantic Web challenges<br />
  52. Companies range from small, family-owned businesses to massive global conglomerates. But the challenges faced by even the largest corporation pale in comparison to the scope of the challenges of building a world-wide Semantic Web.<br />
  53. Economic Model<br />What sustains Semantic Web applications in industry?<br />What sustains the Linked Data Web?<br />Are there viable economic models for Linked Data?<br />
  54. Big Issue: Motivation<br />Retailers have clear motivation to put their data on the Web. But…<br />…what if your business is data?<br />Thomson Reuters, Bloomberg, …<br />…what if your business is your application?<br />Facebook, LinkedIn, Yelp, …<br />
  55. Scale<br />Web<br />Fortune 100 corp.<br />
  56. Data Quality<br />Web 1.0 & 2.0 by necessity put a human between the information and its interpretation<br />Web 3.0 queries, searches, and agents seek to automate this<br /><ul><li>Data quality is a challenge to automation</li></li></ul><li>Variable quality of uninterpreted source data<br />What are the highest cities in the US?<br />Variable quality of links and assertions about Linked Data<br />Data Quality – Two Issues<br />405,696,000m<br />
  57. Data Quality – Two Issues<br />What ensures data quality on the Linked Data Web?<br />Enterprises spend millions on data quality already<br />Knowledge management<br />Master data management<br />Governance and curation processes<br />…though data quality issues do seep in when enterprises use Semantic Web to link to partners and public sources of data!<br />
  58. Trust<br />How do we know which contributions to the Linked Data Web to trust?<br />Trust (distrust) the contributors?<br />Trust (distrust) the contributions?<br />Trust (distrust) the process?<br />How is trust established within an enterprise’s Linked Data Web?<br />
  59. Adoption<br />Suggestion: Progress towards enterprise linked data requires far fewer people embrace Semantic Web technologies compared with a global Linked Data Web<br />
  60. Other Challenges<br />Data licensing<br />Open world assumption<br />Unique name assumption<br />Temporal data<br />What other challenges can you think of?<br />
  61. lee@cambridgesemantics.comTo learn more or to discuss the contents of this presentation, please contact me.<br />
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This was a lecture I presented at Professor Stuart Madnick's class, "Evolution Towards Web 3.0" at the MIT Sloan School of Management on April 21, 2011. Please follow along with the speaker notes which add significant commentary to the slides.


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