Semantic Infrastructure Workshop Applications

Tom ReamyChief Knowledge Architect

KAPS GroupKnowledge Architecture Professional Services

http://www.kapsgroup.com

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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

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A Semantic Infrastructure Approach to SearchElements 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

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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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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

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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

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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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Rich Search ResultsDesign 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

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Rich Search ResultsDesign 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

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Rich Search ResultsAutomatic 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

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Semantic Infrastructure for Search Based AppsMultiple 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!

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Semantic Infrastructure for Search AppsMultiple Applications SIRI – Travel Assistant

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

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Case Study – Categorization & Sentiment

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Case Study – Categorization & Sentiment

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Sentiment AnalysisDevelopment 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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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

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Case Study – Taxonomy Development

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Case Study – Taxonomy Development

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Case Study – Taxonomy Development

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Semantic Infrastructure ApplicationsExpertise 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

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Semantic Infrastructure ApplicationsExpertise 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

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Semantic Infrastructure ApplicationsExpertise 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

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Semantic Infrastructure ApplicationsExpertise 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

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Expert General

Research (context dependent) Kid

Statistical Pay

Program performance Classroom

Protocol Fail

Adolescent Attitudes Attendance

Key academic outcomes School year

Job training program Closing

American Educational Research Association Counselor

Graduate management education Discipline

Education Terms

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Expert GeneralMouse Cancer

Dose Scientific

Toxicity Physical

Diagnostic Consumer

Mammography Cigarette

Sampling Smoking

Inhibitor Weight gain

Edema Correct

Neoplasms Empirical

Isotretinion Drinking

Ethylene Testing

Significantly Lesson

Population-base Knowledge

Pharmacokinetic Medicine

Metabolite Sociology

Polymorphism Theory

Subsyndromic Experience

Radionuclide Services

Etiology Hospital

Oxidase Social

Captopril Domestic

Pharmacological agents

Dermatotoxicity

Mammary cancer model

Biosynthesis

Healthcare Terms

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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, customers 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

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Beyond Sentiment: Behavior PredictionCase 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

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Beyond SentimentBehavior 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

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Beyond Sentiment - Wisdom of CrowdsCrowd 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

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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+

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

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Semantic Infrastructure ApproachConclusions 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

Questions? Tom Reamy

tomr@kapsgroup.comKAPS Group

Knowledge Architecture Professional Serviceshttp://www.kapsgroup.com

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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

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Resources Conferences – Web Sites

– Text Analytics World– http://www.textanalyticsworld.com

– Text Analytics Summit– http://www.textanalyticsnews.com

– Semtech– http://www.semanticweb.com

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Resources Blogs

– SAS- http://blogs.sas.com/text-mining/ Web Sites

– Taxonomy Community of Practice: http://finance.groups.yahoo.com/group/TaxoCoP/

– LindedIn – Text Analytics Summit Group– http://www.LinkedIn.com– Whitepaper – CM and Text Analytics -

http://www.textanalyticsnews.com/usa/contentmanagementmeetstextanalytics.pdf

– Whitepaper – Enterprise Content Categorization strategy and development – http://www.kapsgroup.com

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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