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

1. Smart Content = Smart Business Seth Grimes Alta Plana Corporation +1 301-270-0795 @sethgrimes July 6, 2011

2. Table of Content: Perspective Semantics Content analytics Smart Content Business Warning: This is going to be a big-picture talk, and my personal primary focus is not CMS. Also, I don’t plan to talk about RDF, RDFa, microformats, Schema.org, etc., but we can discuss that stuff in Q&A.

3. Perspective changed Western art, for the artist and for the viewer. http://upload.wikimedia.org/wikipedia/commons/d/de/Reconstruction_of_the_temple_of_Jerusalem.jpg http://upload.wikimedia.org/wikipedia/commons/thumb/3/31/La_scuola_di_Atene.jpg/800px-La_scuola_di_Atene.jpg

4. Semantic computing changes our perspective. The Far Side by Gary Larson Ken Jennings, IBM Watson, and Brad Rutter play Jeopardy! https://secure.wikimedia.org/wikipedia/en/wiki/File:Watson_Jeopardy.jpg

5. From here to there. http://www.businessweek.com/magazine/content/04_19/b3882029_mz072.htm

6. The destination? 2001: A Space Odyssey, Stanley Kubrick

7. I see three categories of data: Quantities, whether measured, observed, or computed. Content, which I’ll characterize as non-quantitative information. Metadata describing quantities and content. Structured/unstructured is a false dichotomy.

8. In the CMS/KMS context, content = Stuff your community creates. Stuff you publish. Stuff your community/stakeholders see. Stuff = Documents and messages. Networks. Knowledge. ??? Form is text, media, multi-media, metadata.

9. Intelligent computing involves: Big (and little) Data Analytics Semantics elements of Smart Content Integration Inference Smart Content has been analyzed, structured, tagged, and managed in a fashion that maximizessearch-findability, usability, and usefulness. }

10. Analytics seeks structure in “unstructured” sources x(t) = t y(t) = ½ a (et/a + e-t/a) = acosh(t/a) http://www.tropicalisland.de/NYC_New_York_Brooklyn_Bridge_from_World_Trade_Center_b.jpg http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg

11. Text analytics models text “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.” -- H.P. Luhn, The Automatic Creation of Literature Abstracts, IBM Journal, 1958. http://wordle.net

12. Document input and processing Knowledge handling is key Desk Set (1957): Computer engineer Richard Sumner (Spencer Tracy) and television network librarian Bunny Watson (Katherine Hepburn) and the "electronic brain" EMERAC. Hans Peter Luhn “A Business Intelligence System” IBM Journal, October 1958

14. Brand & reputation management.

15. Marketing, market research & competitive intelligence.

16. Intelligence, counter-terrorism & law enforcement.

17. Life sciences including pharma drug discovery.

18. Risk, fraud, compliance & e-discovery.

19. Media & publishing including social-media analysis and contextual advertizing.

20. Search, still online’s “killer app.”

21. Semantic computing and the Semantic Web.Most of this is beyond-publishing analytics.

23. Extracting & integrating information.

24. Managing and exploiting knowledge.We share online and on-social content goals.

25. Content consumers want fast, direct information access. Content producers – online, social, enterprise – seek voice, visibility, authority, and profit. http://blog.hubspot.com/blog/tabid/6307/bid/14953/What-Do-76-of-Consumers-Want-From-Your-Website-New-Data.aspx, courtesy of Mike Volpe.

27. Three perspectives – Shared goals. How to reach them? Content ConsumerContent Producer shopping selling learning informing speaking listening connecting engaging Content Publishing Platform semantics for structure + findability + usability CONVERSION STICKINESS RESPONSIVENESS SATISFACTION

28. The goal is not to accelerate old approaches. We want to find a better way. “If I’d asked people what they wanted, they would have said a faster horse.” -- Henry Ford ttp://www.autolife.umd.umich.edu/Labor/L_Overview/Ford_Assembly_Lines.htm

29. Smart Content business & technical challenges – Semanticize content. Use – and allow use of – semanticized content. Open systems to use of external, semanticized content. Align your content strategy with existing and emerging business needs.

30. Semantics enablesbetter content production, management & use. Semantics captures – Meaning Relationships Context Understanding –the sense of “unstructured” online, social, and enterprise information, for content consumers and publishers. But there’s much more to semantics than just entities and URIs...

31. Opinion Entities Anaphora / coreference: “They” Events Concepts New York Times, September 8, 1957

32. My 2009 text-analytics market survey asked, [What information] do you need (or expect to need) to extract or analyze: Text Analytics 2009: User Perspectives on Solutions and Providers

33. Semantic Search (eleven types): Faceted search. Related searches. Concept search. Reference-enriched results. Semantically annotated results. Breakthrough Analysis: Two + Nine Types of Semantic Search, http://informationweek.com/news/software/bi/222400100 6. Full-text similarity search; 7. Search on annotations; 8. Ontology-based search; 9. Semantic Web search; 10. Clustered results; 11. Natural language search. Top 5 are the key to a better user experience (UX) and to stickiness and conversion.

34. Beyond search, content exploration. Decisive Analytics http://www.dac.us/

35. Smart Content relies on: Semantic annotation and metadata extraction. Semantic integration, enrichment & analysis. Structuring & management to promote reuse. Smart Content provides: Workflow embedded delivery. Enhanced information access. Smart Content delivers: Customer satisfaction. Competitiveness. Profitability. Insight.

36. So a couple of beyond-CMS/KMS business challenges– Facilitate the inclusion & integration of enterprise & Web content, and social & enterprise data, into the ensemble of systems your organization supports and uses. Innovate.

38. Question answering.

39. Entity/identity resolution & profile extraction.

40. Online-social-enterprise data integration.

41. Speech analytics.

42. Discourse analysis.

43. Rich-media content analytics.

44. Augmented reality; new human-computer interfaces.

45. Semantic search