Smart Content = Smart Business


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Presentation by Seth Grimes at the IKS Semantic Workshop, July 6, 2011.

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Smart Content = Smart Business

  1. 1. Smart Content = Smart Business<br />Seth Grimes<br />Alta Plana Corporation<br />+1 301-270-0795<br />@sethgrimes<br />July 6, 2011<br />
  2. 2. Table of Content:<br />Perspective<br />Semantics<br />Content analytics<br />Smart Content<br />Business<br />Warning: This is going to be a big-picture talk, and my personal primary focus is not CMS.<br />Also, I don’t plan to talk about RDF, RDFa, microformats,, etc., but we can discuss that stuff in Q&A.<br />
  3. 3. Perspective changed Western art, for the artist and for the viewer.<br /><br />
  4. 4. Semantic computing changes our perspective.<br />The Far Side <br />by Gary Larson<br />Ken Jennings, IBM Watson, and Brad Rutter play Jeopardy!<br /><br />
  5. 5. From here to there.<br /><br />
  6. 6. The destination?<br />2001: A Space Odyssey, Stanley Kubrick<br />
  7. 7. I see three categories of data:<br />Quantities, whether measured, observed, or computed.<br />Content, which I’ll characterize as non-quantitative information.<br />Metadata describing quantities and content.<br />Structured/unstructured is a false dichotomy.<br />
  8. 8. In the CMS/KMS context, content =<br />Stuff your community creates.<br />Stuff you publish.<br />Stuff your community/stakeholders see.<br />Stuff =<br />Documents and messages.<br />Networks.<br />Knowledge.<br />???<br />Form is text, media, multi-media, metadata.<br />
  9. 9. Intelligent computing involves:<br />Big (and little) Data<br />Analytics<br />Semantics elements of Smart Content<br />Integration<br />Inference<br />Smart Content has been analyzed, structured, tagged, and managed in a fashion that maximizessearch-findability, usability, and usefulness.<br />}<br />
  10. 10. Analytics seeks structure in “unstructured” sources<br />x(t) = t <br />y(t) = ½ a (et/a + e-t/a)<br /> = acosh(t/a)<br /><br /><br />
  11. 11. Text analytics models text<br />“Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences.”<br />-- H.P. Luhn, The Automatic Creation of Literature Abstracts, IBM Journal, 1958.<br /><br />
  12. 12. Document input and processing<br />Knowledge handling is key<br />Desk Set (1957): Computer engineer Richard Sumner (Spencer Tracy) and television network librarian Bunny Watson (Katherine Hepburn) and the "electronic brain" EMERAC.<br />Hans Peter Luhn<br />“A Business Intelligence System”<br />IBM Journal, October 1958<br />
  13. 13. Where there’s text, there’s business value:<br /><ul><li>Customer service/support.
  14. 14. Brand & reputation management.
  15. 15. Marketing, market research & competitive intelligence.
  16. 16. Intelligence, counter-terrorism & law enforcement.
  17. 17. Life sciences including pharma drug discovery.
  18. 18. Risk, fraud, compliance & e-discovery.
  19. 19. Media & publishing including social-media analysis and contextual advertizing.
  20. 20. Search, still online’s “killer app.”
  21. 21. Semantic computing and the Semantic Web.</li></ul>Most of this is beyond-publishing analytics.<br />
  22. 22. Everyone is a content consumer.<br />… at home, at work, and on the move (mobile).<br />Anyone can be a content producer.<br />… thanks to computers, devices, the Web, and social content publishing platforms.<br />We share technical needs – <br />Usable tools to automate the jobs of:<br /><ul><li>Producing, publishing & finding content.
  23. 23. Extracting & integrating information.
  24. 24. Managing and exploiting knowledge.</li></ul>We share online and on-social content goals.<br />
  25. 25. Content consumers want fast, direct information access.<br />Content producers – online, social, enterprise – seek voice, visibility, authority, and profit.<br />, courtesy of Mike Volpe.<br />
  26. 26. From a 2011 study on Journal Article Mining by the Publishing Research Consortium (via TEMIS), of Publishers of Scientific Journals, 46% semantically enrich content.<br /><ul><li>82% to make content more compelling</li></ul>Improved Search & Navigation<br />Semantic Linking to related content & knowledge<br />Visual Analytics<br /><ul><li>57% to create new products& services</li></ul>Knowledge Bases<br />Topic Pages<br />Contextual Advertising<br />
  27. 27. Three perspectives – Shared goals.<br />How to reach them?<br />Content ConsumerContent Producer<br />shopping selling<br />learning informing<br />speaking listening<br />connecting engaging<br />Content Publishing Platform<br />semantics for structure + findability + usability<br />CONVERSION<br />STICKINESS<br />RESPONSIVENESS<br />SATISFACTION<br />
  28. 28. The goal is not to accelerate old approaches.<br />We want to find a better way.<br />“If I’d asked people what they wanted, they would have said a faster horse.” -- Henry Ford<br /><br />
  29. 29. Smart Content business & technical challenges –<br />Semanticize content.<br />Use – and allow use of – semanticized content.<br />Open systems to use of external, semanticized content.<br />Align your content strategy with existing and emerging business needs.<br />
  30. 30. Semantics enablesbetter content production, management & use. <br />Semantics captures –<br />Meaning<br />Relationships<br />Context <br />Understanding<br />–the sense of “unstructured” online, social, and enterprise information, for content consumers and publishers.<br />But there’s much more to semantics than just entities and URIs...<br />
  31. 31. Opinion<br />Entities<br />Anaphora / coreference: “They”<br />Events<br />Concepts<br />New York Times,<br />September 8, 1957<br />
  32. 32. My 2009 text-analytics market survey asked, [What information] do you need (or expect to need) to extract or analyze:<br />Text Analytics 2009: User Perspectives on Solutions and Providers<br />
  33. 33. Semantic Search (eleven types):<br />Faceted search.<br />Related searches.<br />Concept search.<br />Reference-enriched results.<br />Semantically annotated results.<br />Breakthrough Analysis: Two + Nine Types of Semantic Search,<br />6. Full-text similarity search; 7. Search on annotations; 8. Ontology-based search; 9. Semantic Web search; 10. Clustered results; 11. Natural language search.<br />Top 5 are the key to a better user experience (UX) and to stickiness and conversion.<br />
  34. 34. Beyond search, content exploration.<br />Decisive Analytics<br /><br />
  35. 35. Smart Content relies on:<br />Semantic annotation and metadata extraction.<br />Semantic integration, enrichment & analysis. <br />Structuring & management to promote reuse.<br />Smart Content provides:<br />Workflow embedded delivery.<br />Enhanced information access.<br />Smart Content delivers:<br />Customer satisfaction.<br />Competitiveness.<br />Profitability.<br />Insight. <br />
  36. 36. So a couple of beyond-CMS/KMS business challenges–<br />Facilitate the inclusion & integration of enterprise & Web content, and social & enterprise data, into the ensemble of systems your organization supports and uses.<br />Innovate.<br />
  37. 37. Innovation is essential. In content-analytics:<br /><ul><li>Advanced sentiment analysis: emotions, opinions & intent.
  38. 38. Question answering.
  39. 39. Entity/identity resolution & profile extraction.
  40. 40. Online-social-enterprise data integration.
  41. 41. Speech analytics.
  42. 42. Discourse analysis.
  43. 43. Rich-media content analytics.
  44. 44. Augmented reality; new human-computer interfaces.
  45. 45. Semantic search
  46. 46. Web 3.0 & the Semantic Web.</li></li></ul><li>A few references:<br />Six Definitions of Smart Content, InformationWeek, September 24, 2010.<br />This is Content Intelligence, According to 4 Experts, CMSwire, October 7, 2010. <br />Content Management Finds Meaning, EContent, October 12, 2010.<br />Smart Content Conference videos, October 2010:<br />What’s your vision of Smart Content?<br />