Keynote presentation from Stephane Croisier at the July 11 IKS Workshop on moving from semantic platforms to semantic applications

1. From Semantic Platforms to Semantic Apps Time to focus on creatingthe nextgeneration of compellingsemanticexperience

2. By @scroisier

3. Email: scroisier@gmail.com

4. Sponsored by

5. I – 2011 Trends

6. Whatischanging over the last years? Massive Explosion of Raw Unstructured Content

7. Email Explosion

8. Document Explosion

9. Social Content Explosion

10. And nowit *REALLY* starts to impact all of us

11. “The volume of information flowing through organizations grows by200% per year” “80% of data is unstructured, doubling every month” “Fortune 1000 stands to waste at least $2.5 billion per year due to an inability to locate and retrieve information” “68% of employees recognize that it is difficult and time consuming to find information needed to do their job.”

15. Infoglut’ssyndroms Procrastination Unefficiencies Overwhelming Frustration

16. Have we reached an inflection point? Are we assisting to a Search and Information Access Revival?

17. System of Record System of Engagement System of Access Command & Control Collaborative Curate & Filter Transaction-oriented Interaction-oriented Link-oriented Reference-centric Data-centric User-centric Consistency/Relevancy is a key issue Security is a key issue Privacy is a key issue Web 3.0 Web 1.0 Web 2.0 Social Information Access Content Lifecycle

18. II – Problems

19. Where are we in 2011? Mobility Social Cloud Semantic CMS Unlikely Match?

20. Semanticsmeanstoomanydifferentthings for toomanypeople

21. The One Week Reality Check

22.  Monday / TechnicalFragmentation

23. “As of today, there are probably as many architectures as there are vendors in the domain. ” Dr Mario Lenz

24.  Tuesday / MarketOpacity

25. “Expect consolidation, in a market with over 50 players, the exact solution for a given business problem might be a moving target for some time yet.” John Harney - KMWorld

26. Wednesday/ Complexity

27. “Any practical feedback on LSH with Random Projections vs LSH with MinHash for text clustering / duplication detection?” Tweetfrom Olivier Grisel Nuxeo

28. © Billy Cripe

29.  Thursday / Lack of MultilanguageReadiness Most semantic technologies are stillonlyavailable in English

30. “Full Multingual Search: Holy Grail and still in a distant future.” Prof. Dr. Stefan Gradmann about the «Europeana» project - 2009

31.  Friday / Scalability Issues

32. “The systems that do that today are slow and clunky and don’t scale to the kinds of data sets we do with – they’re not good for 150 million messages a day.” Nova Spivack

33. Saturday / Fanatism

34. “A world of exhaustive, reliable metadata would be a utopia. It's also a pipe-dream, founded on self-delusion, nerd hubris and hysterically inflated market opportunities. ” Cory Doctorow

35. Overselling Sunday / VendorOverselling Overexpectations Overpromises

36. “You can invest a lot and not get any return.” Scott Brinker (in Top 10 reasons the Semantic Web is a lot like love)

37. We are still at this stage

38. Whenwe all expectedto bealreadyhere

39. Lots of promises But move more slowly than expected

40. Semanticcost+complexity vs user centeredadded value isstilla toofoggy story Problems being solved are too theorical Practical short-time client deliverables are still confused Level of skills required prevent widespread adoption

41. III–From Semantic Platforms To Semantic Apps

42. Semantic Platforms vs Semantic Apps Smart Email Capabilities EnhancedSearchExperience DynamicLanding Pages RelatedContent Widget Contextual Content Discovery Plug-ins Personalized Content Feeds AdsTargetting Latent SemanticAnalysis RDF/OWL Ontologies Entities Natural LanguageProcessing RESTfulAPI Data Schema Tripples

43. From the Geek to the Practitioner

44. From a monocultural stack To a largerparadigm?

45. How technical research areas are approaching text and semantics? Semantics Social Network Analysis graphs / networks Computational Linguistics Text Mining Information Retrieval Semantic Web Machine Learning Web2.0 interoperability language analytics statistics search community © Marko Grobelnik – Jozef Stefan Institute

46. How do I approach Semantics?

47. Content & Data Interoperability

48. Content Intelligence Middleware

49. Smart Content Apps

50. Smart SEO/Ads Personalized Content Delivery Smart Features DynamicTopical Pages Intelligent Search Etc. User interfaces and applications Trust Proof Unifying Logic Social Intelligence Ontologies: OWL Rules: RIF/SWRL Querying Business Intelligence Cryptography Content Intelligence Middleware SPARQL SQL XPATH Vector Space Models Taxonomies: RDFS Inverted Keyword Index NLP Data Interchange CMIS CMIS RDF EDI Syntax CSV TXT XML JSON Protocol REST SOAP WEBDAV SMTP Data Interoperability Identifier PATH URI Character Set UNICODE BINARY Persistance NoSQL DB SQL DB TrippleStore

51. Content & Data Interoperability

52. RDFa Whatisseeingcurrently the mostuse

53. Big Data Think Massive Volumes / Lots of Format

54. “RDF might be a very good choice for how to make that available, but it can’t be a religious choice. We have to look at the problem specifically and ask how does RDF or XML or anything else map to it. We’d consider it as a tool in our toolbox.” Nova Spivack

55. “The infrastructure needs of intelligent systems are now being met by a combination of Semantic Web, Linked Data, Web Services and Rule-based systems.” Jim Hendler - RPI

56. Job in progress.

57. Content Intelligence Middleware

58. Why the « Intelligence » layer does not take off?

59. “Ontology is Overrated!” Clay Shirky - 2005 The Ontology Story isstillconfused Most usage are still more « expert » systems than generic solutions

60. ` The «smart» section of the Semantic Web: onlya fraction of the whole story?

61. Emergence of a new « Content Intelligence » industry

62. Content Intelligence Social and Behavioural Intelligence Business Intelligence At the intersection of EntepriseSearch, BI, Analytics, Social and Content Management

63. Embracing the promise of turninganyrawdata into smart content Automated Information Clustering TextMining Content Analytics Connections to Linked Data Repo Rule-based classification Natural LanguageProcessing Content Enrichment An InfiniteComplexityProblem?

64. The All-Purposed Platform Issue Intellectually satisfying, concretely feasible?

65. Content Intelligence Middleware: To be or not to be?

66. Onlyone certainty: Think « Smart Inside » ratherthansimply « Semantic Web Ready »

67. Smart Content Apps

68. And finallywhy are weimplementingthiswholestack?

69. Main Challenge: Finding the next Semantic Killer App

70. “10+ years into its inception, Semantic Web still has no clear killer app. It’s not clear if or when that app will emerge.” James KobielusForrester Group

72. “User Interfaces for Semantic Web: Do They Have to Be Ugly?” Andraz Tori, Zemanta – SemTech 2010

73. Never that any more!

75. “Let’s get serious about user experience design. It means utilizing your best engineers for more than just coding. “ Marty Cagan

76. Refocus on enabling the nextSemanticExperience Emotional Fun-to-use User-centered Addictive Pleasurable Contextual

78. “Somehow, I repeatedly run into a situation where some use of Semantic Web technologies that would make a nice end-user application is blocked by the fact that the user interface is the real challenge.” OraLassila - 2007

79. Amounts invested in Semantic UX vsSemantic Tech Stat source: Gut-feeling of Stephane Croisier

80. MultidiciplinaryTeam Needed UX Guru Big Data Rock Star Semantic Jedi Polyglot Analytics Ninja MarketLurker and Sniffer Content Management Hero

81. IV – Smart CMS

82. MatchingSemantic Technologies withInformation Lifecycle and User Experience

83. A few Semantic User Stories braught to you by Ongoing Internal R&D Efforts

84. How to empower a CMS…

85. … by bringingnew semantic value added?

87. Analyze your Information DNA 1 Let the user better know about your content assets

88. Content Enhancements Keywords Concepts Tags Categories Automatically enrich your unstructured content

89. Automatically enrich your unstructured content

90. Entity Extraction

91. Auto-Classification (Common Tags / Concepts / Categories)

92. Or providesemi-automated guidance for editors

93. SearchEngine Enhancements 2 Let the user findratherthansearch

94. SearchEnhancements & Automated SEO calibration

95. Dynamic Navigations 3 Let the user explore instead of searching

96. Drill-down, Filter and Mine into your information universe

97. Semantic Navigation Components

98. DynamicGlossary

99. URL-FriendlyTopical Landing Pages

100. Conceptual Match 4 Let the user find what he wants, not type

101. ConceptualMatch Get the needles out of the haystack without typing

102. Getrid of keywords limitations

103. Enhanced Search Box

106. Liquid Information Discovery 5 Let the user stick on your site

107. In-Context Content Discovery Contextual Access to Public or PrivateFacingLinkedData Repositories Discoveryengine to similar Content in pre-selectedContent Stores

108. « It’s like Augmented Reality for browsing the web. » Marshall Kirkpatrick, RWW

109. Semantic Intertextualities

110. 6 Ambiant Findability Let the user come back to your site

112. Contextual Similarities Access to the information your need right from within your context of use

113. 7 Surface the best Automaticallylearnfromusers and provides a personalizedexperience

114. Surface the Best Content Recommendations/Predictions according to YOUR interests

115. Let the user favourite, subscribe or « like » items To auto-populate its own information DNA

116. And automaticallyget new articles similar to yourinterests…

117. 8 Curate the rest Let userscreate and sharetheirown Search-based Application

118. FastGrowth of Semantic-ready and Search-focusedMicro-Sites

119. After the explosion of Blog, Wiki and ActivityStreams, the rise of CuratedSearchSpaces?

120. Conclusion ( Standardized Data Interop layer + Content Intelligence Middleware + Smart Features) xCompelling User Experience _______________________ Smart Content Apps for Tomorrow

121. Thanks for watching

122. Information Most images from www.flickr.com (Image & licensing info in the notes section of slides) Presentation licensed: Creative Commons BY-NC-SA The presentation can be downloaded from: www.slideshare.net/scroisier More information about me: http://www.linkedin.com/in/scroisier