Semantic Infrastructure Workshop Applications
Tom ReamyChief Knowledge Architect
KAPS Group
Knowledge 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
Conclusions
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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
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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 – Social – Hybrid folksonomy / taxonomy / auto-metadata– Social – expertise, categorize tweets and blogs, reputation– Ontology – travel assistant – SIRI
Use your Imagination!
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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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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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 General
Mouse 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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Semantic Infrastructure ApplicationsExpertise Analysis – application areas Taxonomy/ Ontology development /design – use basic level User contribution
– Card sorting – non-experts use superficial similarities– Survey for attributes instead of cart sorting, general structure
Develop expert and general versions/sections/synonyms Info presentation – combine superordinate and basic
– Similar to scientific – Genus – Species is official name Text Mining
– Expertise characterization of writer
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Semantic Infrastructure ApplicationsExpertise Analysis – application areas Business & Customer intelligence
– General – characterize people’s expertise to add to evaluation of their comments
– Combine with sentiment analysis – finer evaluation – what are experts saying, what are novices saying
– Deeper research into communities, customers Enterprise Content Management
– At publish time, software automatically gives an expertise level – present to author for validation
– Combine with categorization – offer tags that are suitable level of expertise
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Semantic Infrastructure ApplicationsExpertise Analysis – application areas Social Media - Community of Practice
– Characterize the level of expertise in the community– Evaluate other communities expertise level– Personalize information presentation by expertise
Expertise location– Generate automatic expertise characterization based on
authored documents Expertise of people in a social network
– Terrorists and bomb-making
Semantic Infrastructure ApplicationsExpertise Analysis – application areas- CoP Basic Level
– Blog– Software (Design)– Web (Design)– Linux– Javascript– Web2.0– Google– Css– Flash
Superordinate– Music– Photography– News– Education– Business– Technology– Politics– Science– Culture
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Semantic Infrastructure ApplicationsExpertise Analysis – application areas-Tags CSS
– Web Design– Design– Css3– Tutorial– Webdev– Javascript– Web– Development– Html– Jquery– html5
Education– Technology– Resources– Teaching– Learning– Science– Web20– Games– Interactive– Research– Tools– reference
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Semantic Infrastructure Approach to SearchConclusions Semantic Infrastructure solution (people, policy, technology,
semantics) and feedback is best approach Foundation – Hybrid ECM model with text analytics, Search Integrated Search design is essential – rich results
– Subject, facets, tag clouds, etc. Semantic Infrastructure as a platform for multiple applications
– Build on infrastructure for economy and quality Text Analytics (Entity extraction and auto-categorization) 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?
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
39
Resources
Books– Women, Fire, and Dangerous Things
• George Lakoff– Knowledge, Concepts, and Categories
• Koen Lamberts and David Shanks Web Sites
– Text Analytics News - http://social.textanalyticsnews.com/index.php
– Text Analytics Wiki - http://textanalytics.wikidot.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
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