1. Semantic Infrastructure Workshop Applications Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2. 2 Agenda  Search and Semantic Infrastructure – Elements /Rich Dynamic Results – Different Environments – Design Issues  Platform for Information Applications – Multiple Applications – Case Study – Categorization & Sentiment – Case Study – Taxonomy Development – Case Study – Expertise & Sentiment & Beyond  Conclusions

3. 3 A Semantic Infrastructure Approach to Search Elements  Multiple Knowledge Structures – Facet – orthogonal dimension of metadata – Taxonomy - Subject matter / aboutness – Ontology – Relationships / Facts Subject – Verb - Object  Software - Search, ECM, auto-categorization, entity extraction, Text Analytics and Text Mining  People – tagging, evaluating tags, fine tune rules and taxonomy  People – Users, social tagging, suggestions  Rich Search Results – context and conversation

4. 4 A Semantic Infrastructure Approach to Search: Rich Results  Elements – Faceted Navigation – Categorization – metadata and/or dynamic – Tag Clouds – clustering – User Tags, personalization – Related topics – discovery  Supports all manner of search behaviors and needs – Find known items – zero in with facets – Discovery – Tags clouds, user tags, related topics – Deep dive - categorization

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8. 8 A Semantic Infrastructure Approach to Search: Three Environments  E-Commerce – Catalogs, small uniform collections of entities – Conflict of information and Selling – Uniform behavior – buy this  Enterprise – More content, more types of content – Enterprise Tools – Search, ECM – Publishing Process – tagging, metadata standards  Internet – Wildly different amount and type of content, no taggers – General Purpose – Flickr, Yahoo – Vertical Portal – selected content, no taggers

9. 9 A Semantic Infrastructure Approach to Search: Enterprise Environment –Taxonomy, 7 facets  Taxonomy of Subjects / Disciplines: – Science > Marine Science > Marine microbiology > Marine toxins  Facets: – Organization > Division > Group – Clients > Federal > EPA – Instruments > Environmental Testing > Ocean Analysis > Vehicle – Facilities > Division > Location > Building X – Methods > Social > Population Study – Materials > Compounds > Chemicals – Content Type – Knowledge Asset > Proposals

10. 10 A Semantic Infrastructure Approach to Search: Internet Design  Subject Matter taxonomy – Business Topics – Finance > Currency > Exchange Rates  Facets – Location > Western World > United States – People – Alphabetical and/or Topical - Organization – Organization > Corporation > Car Manufacturing > Ford – Date – Absolute or range (1-1-01 to 1-1-08, last 30 days) – Publisher – Alphabetical and/or Topical – Organization – Content Type – list – newspapers, financial reports, etc.

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12. 12 Rich Search Results Design Issues - General  What is the right combination of elements? – Faceted navigation, metadata, browse, search, categorized search results, file plan  What is the right balance of elements? – Dominant dimension or equal facets – Browse topics and filter by facet  When to combine search, topics, and facets? – Search first and then filter by topics / facet – Browse/facet front end with a search box

13. 13 Rich Search Results Design Issues - General  Homogeneity of Audience and Content  Model of the Domain – broad – How many facets do you need? – More facets and let users decide – Allow for customization – can’t define a single set  User Analysis – tasks, labeling, communities Issue – labels that people use to describe their business and label that they use to find information  Match the structure to domain and task – Users can understand different structures

14. 14 Rich Search Results Automatic Facets – Special Issues  Scale requires more automated solutions – More sophisticated rules  Rules to find and populate existing metadata – Variety of types of existing metadata – Publisher, title, date – Multiple implementation Standards – Last Name, First / First Name, Last  Issue of disambiguation: – Same person, different name – Henry Ford, Mr. Ford, Henry X. Ford – Same word, different entity – Ford and Ford  Number of entities and thresholds per results set / document – Usability, audience needs  Relevance Ranking – number of entities, rank of facets

15. 15 Semantic Infrastructure for Search Based Apps Multiple Applications  Platform for Information Applications – Content Aggregation – Duplicate Documents – save millions! – Text Mining – BI, CI – sentiment analysis – Combine with Data Mining – disease symptoms, new Predictive Analytics – Social – Hybrid folksonomy / taxonomy / auto-metadata – Social – expertise, categorize tweets and blogs, reputation – Ontology – travel assistant – SIRI  Use your Imagination!

16. 16 Semantic Infrastructure for Search Apps Multiple Applications  SIRI – Travel Assistant

17. Semantic Infrastructure for Search Apps Case Study – Categorization & Sentiment  Call Motivation – Categorization – Motivation Taxonomy – Purpose of previous calls to understand current call – Issues of scale, small size of documents, jargon, spelling  Customer Sentiment – Telecom Forums – Feature level – not just products – Issue of context - sarcasm, jargon  Knowledge Base – Categorization, Product extraction, expertise-sentiment analysis – Social Media as source for solutions 17

18. Case Study – Categorization & Sentiment 18

19. Case Study – Categorization & Sentiment 19

20. 20 Sentiment Analysis Development Process  Combination of Statistical and categorization rules  Start with Training sets – examples of positive, negative, neutral documents  Develop a Statistical Model  Generate domain positive and negative words and phrases  Develop a taxonomy of Products & Features  Develop rules for positive and negative statements  Test and Refine  Test and Refine again

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26. Semantic Infrastructure for Search Apps Case Study – Taxonomy Development  Problem – 200,000 new uncategorized documents  Old taxonomy –need one that reflects change in corpus  Text mining, entity extraction, categorization  Content – 250,000 large documents, search logs, etc.  Bottom Up- terms in documents – frequency, date,  Clustering – suggested categories  Clustering – chunking for editors  Entity Extraction – people, organizations, Programming languages  Time savings – only feasible way to scan documents  Quality – important terms, co-occurring terms 26

27. Case Study – Taxonomy Development 27

28. Case Study – Taxonomy Development 28

29. Case Study – Taxonomy Development 29

30. 30 Semantic Infrastructure Applications Expertise Analysis  Sentiment Analysis to Expertise Analysis(KnowHow) – Know How, skills, “tacit” knowledge  No single correct categorization – Women, Fire, and Dangerous Things – Types of Animals Those that belong to the Emperor Embalmed Ones Suckling Pigs Fabulous Ones Those that are included in this classification Those that tremble as if they were mad Other

31. 31 Semantic Infrastructure Applications Expertise Analysis – Basic Level Categories  Mid-level in a taxonomy / hierarchy  Short and easy words  Maximum distinctness and expressiveness  First level named and understood by children  Level at which most of our knowledge is organized  Levels: Superordinate – Basic – Subordinate – Mammal – Dog – Golden Retriever – Furniture – chair – kitchen chair

32. 32 Semantic Infrastructure Applications Expertise Analysis  Experts prefer lower, subordinate levels – In their domain, (almost) never used superordinate  Novice prefer higher, superordinate levels  General Populace prefers basic level  Not just individuals but whole societies / communities differ in their preferred levels  Issue – artificial languages – ex. Science discipline  Issue – difference of child and adult learning – adults start with high level

33. 33 Semantic Infrastructure Applications Expertise Analysis  What is basic level is context(s) dependent – Document/author expert in news health care, not research  Hybrid – simple high level taxonomy (superordinate), short words – basic, longer words – expert Plus  Develop expertise rules – similar to categorization rules – Use basic level for subject – Superordinate for general, subordinate for expert  Also contextual rules – “Tests” is general, high level – “Predictive value of tests” is lower, more expert – If terms appear in same sentence - expert

34. 34 ExpertGeneral Research (context dependent)Kid StatisticalPay Program performanceClassroom ProtocolFail Adolescent AttitudesAttendance Key academic outcomesSchool year Job training programClosing American Educational Research AssociationCounselor Graduate management educationDiscipline Education Terms

35. 35 ExpertGeneral MouseCancer DoseScientific ToxicityPhysical DiagnosticConsumer MammographyCigarette SamplingSmoking InhibitorWeight gain EdemaCorrect NeoplasmsEmpirical IsotretinionDrinking EthyleneTesting SignificantlyLesson Population-baseKnowledge PharmacokineticMedicine MetaboliteSociology PolymorphismTheory SubsyndromicExperience RadionuclideServices EtiologyHospital OxidaseSocial CaptoprilDomestic Pharmacological agents Dermatotoxicity Mammary cancer model Biosynthesis Healthcare Terms

36. 36 Expertise Analysis Expertise – application areas  Taxonomy / Ontology development /design – audience focus – Card sorting – non-experts use superficial similarities  Business & Customer intelligence – add expertise to sentiment – Deeper research into communities, customer s  Text Mining - Expertise characterization of writer, corpus  eCommerce – Organization/Presentation of information – expert, novice  Expertise location- Generate automatic expertise characterization based on documents  Experiments - Pronoun Analysis – personality types – Essay Evaluation Software - Apply to expertise characterization Model levels of chunking, procedure words over content

37. 37 Beyond Sentiment: Behavior Prediction Case Study – Telecom Customer Service  Problem – distinguish customers likely to cancel from mere threats  Analyze customer support notes  General issues – creative spelling, second hand reports  Develop categorization rules – First – distinguish cancellation calls – not simple – Second - distinguish cancel what – one line or all – Third – distinguish real threats

38. 38 Beyond Sentiment Behavior Prediction – Case Study  Basic Rule – (START_20, (AND, – (DIST_7,"[cancel]", "[cancel-what-cust]"), – (NOT,(DIST_10, "[cancel]", (OR, "[one-line]", "[restore]", “[if]”)))))  Examples: – customer called to say he will cancell his account if the does not stop receiving a call from the ad agency. – cci and is upset that he has the asl charge and wants it off or her is going to cancel his act – ask about the contract expiration date as she wanted to cxl teh acct Combine sophisticated rules with sentiment statistical training and Predictive Analytics

39. 39 Beyond Sentiment - Wisdom of Crowds Crowd Sourcing Technical Support  Example – Android User Forum  Develop a taxonomy of products, features, problem areas  Develop Categorization Rules: – “I use the SDK method and it isn't to bad a all. I'll get some pics up later, I am still trying to get the time to update from fresh 1.0 to 1.1.” – Find product & feature – forum structure – Find problem areas in response, nearby text for solution  Automatic – simply expose lists of “solutions” – Search Based application  Human mediated – experts scan and clean up solutions

40. 40 Semantic Infrastructure: A Platform for KM Applications  Expertise Location – Individuals and Communities  Knowledge Sharing – Com. Of Practice – Find right person better – Knowledge representation to support better sharing – Enhance sharing as well as sub for person  Knowledge Base // Portal – Greatly improved – find what you are looking for – New kinds of presentations – rich search to dynamic graphs  Process – deliver rich K representation in work flow – SIRI+

41. Text Analytics: Future Directions  Start with the 80% of significant content that is not data – Enterprise search, content management, Search based applications  Text Analytics and Text Mining – Text Analytics turns text into data – Build better TM Apps – Better extraction and add Subject / Concepts – Sentiment and Beyond – Behavior, Expertise  Text Mining and Text Analytics – TM enriching TA – Taxonomy development – New Content Structures, ensemble models  Text Analytics and Predictive Analytics – More content, New content – social, interactive – CSR – New sources of content/data = new & better apps 41

42. 42 Semantic Infrastructure Approach Conclusions  Semantic Infrastructure solution (people, policy, technology, semantics) and feedback is best approach  Foundation – Hybrid ECM model with text analytics, Search  Integrated information, knowledge, and semantics  Semantic Infrastructure as a platform for multiple applications – Build on infrastructure for economy and quality  Text Analytics (Entity extraction and auto-categorization, sentiment analysis) are essential  Future – new kinds of applications: – Text Mining and Data mining, research tools, sentiment – Beyond Sentiment – expertise applications – NeuroAnalytics – cognitive science meets search and more Watson is just the start

43. Questions? Tom Reamy tomr@kapsgroup.com KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

44. 44 Resources  Books – Women, Fire, and Dangerous Things George Lakoff – Knowledge, Concepts, and Categories Koen Lamberts and David Shanks – Formal Approaches in Categorization Ed. Emmanuel Pothos and Andy Wills – The Mind Ed John Brockman Good introduction to a variety of cognitive science theories, issues, and new ideas – Any cognitive science book written after 2009

45. 45 Resources  Conferences – Web Sites – Text Analytics World – http://www.textanalyticsworld.com http://www.textanalyticsworld.com – Text Analytics Summit – http://www.textanalyticsnews.com http://www.textanalyticsnews.com – Semtech – http://www.semanticweb.com http://www.semanticweb.com

46. 46 Resources  Blogs – SAS- http://blogs.sas.com/text-mining/ http://blogs.sas.com/text-mining/  Web Sites – Taxonomy Community of Practice: http://finance.groups.yahoo.com/group/TaxoCoP/ http://finance.groups.yahoo.com/group/TaxoCoP/ – LindedIn – Text Analytics Summit Group – http://www.LinkedIn.com http://www.LinkedIn.com – Whitepaper – CM and Text Analytics - http://www.textanalyticsnews.com/usa/contentmanagementm eetstextanalytics.pdf http://www.textanalyticsnews.com/usa/contentmanagementm eetstextanalytics.pdf – Whitepaper – Enterprise Content Categorization strategy and development – http://www.kapsgroup.comhttp://www.kapsgroup.com

47. 47 Resources  Articles – Malt, B. C. 1995. Category coherence in cross-cultural perspective. Cognitive Psychology 29, 85-148 – Rifkin, A. 1985. Evidence for a basic level in event taxonomies. Memory & Cognition 13, 538-56 – Shaver, P., J. Schwarz, D. Kirson, D. O’Conner 1987. Emotion Knowledge: further explorations of prototype approach. Journal of Personality and Social Psychology 52, 1061-1086 – Tanaka, J. W. & M. E. Taylor 1991. Object categories and expertise: is the basic level in the eye of the beholder? Cognitive Psychology 23, 457-82