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Basic Level Categories for Knowledge Representation Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services

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Presentation on theme: "Basic Level Categories for Knowledge Representation Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services"— Presentation transcript:

1 Basic Level Categories for Knowledge Representation Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2 2 Agenda  Introduction – Context – Category Theory – Cognitive Science – Enterprise Text Analytics  Basic Level Categories – Features and Issues  Basic Level Categories and Expertise – Experts prefer lower levels – Categorization of Expertise  Applications – Integration with Search and ECM – Platform for Information Applications

3 3 KAPS Group: General  Knowledge Architecture Professional Services  Virtual Company: Network of consultants – 8-10  Partners – SAS, SAP, Microsoft-FAST, Concept Searching, etc.  Consulting, Strategy, Knowledge architecture audit  Services: – Taxonomy/Text Analytics development, consulting, customization – Technology Consulting – Search, CMS, Portals, etc. – Evaluation of Enterprise Search, Text Analytics – Metadata standards and implementation – Knowledge Management: Collaboration, Expertise, e-learning – Applied Theory – Faceted taxonomies, complexity theory, natural categories

4 4 Basic Level Categories Context  Unstructured Content - Enterprise & External  Preprocessing of documents and sets – Includes categorization, information extraction  Representation of Domain knowledge – taxonomy, ontology  Presentation of results of search, text mining – and refinement  Categorization – Most basic to human cognition – Most difficult to do with software  No single correct categorization – Women, Fire, and Dangerous Things

5 5 Basic Level Categories Context  Borges – Celestial Emporium of Benevolent Knowledge – Those that belong to the Emperor – Embalmed ones – Those that are trained – Suckling pigs – Mermaids – Fabulous ones – Stray dogs – Those that are included in this classification – Those that tremble as if they were mad – Innumerable ones – Other

6 6 Basic Level Categories – software context Enterprise Text Analytics (ETA)  Enterprise Search – Faceted Navigation – Categorization – Document Topics – Aboutness – Entity Extraction – noun phrases, feed facets, ontologies – Summarization – beyond snippets  Enterprise Content Management – Hybrid model of metadata – Categorization – suggestions – Entity, Noun phrase – facets need a lot of metadata

7 7 Basic Level Categories – software context Enterprise Text Analytics (ETA)  Advanced Text Analytics – Fact extraction – ontologies – Sentiment Analysis – good, bad, and ugly – Expertise Analysis  Enterprise Applications –Information Applications – Text mining – alone or in conjunction with data mining – Business & Customer intelligence

8 8 Basic Level Categories Introduction: What are Basic Level Categories?  Mid-level in a taxonomy / hierarchy  Short and easy words  Maximum distinctness and expressiveness  Similarly perceived shapes  Most commonly used labels  Easiest and fastest to indentify members  First level named and understood by children  Terms usually used in neutral contexts  Level at which most of our knowledge is organized

9 9 Basic Level Categories Introduction: What are Basic Level Categories?  Objects – most studied, most pronounced effects  Levels: Superordinate – Basic – Subordinate – Mammal – Dog – Golden Retriever – Furniture – chair – kitchen chair  Basic in 4 dimensions – Perception – overall perceived shape, single mental image, fast identification – Function – general motor program – Communication – shortest, most commonly used, neutral, first learned by children – Knowledge Organization – most attributes are stored at this level

10 10 Basic Level Categories Introduction: Basic Level Categories: Non-Object  Basic level effects, but no widespread acceptance of categories and category names  Thus a basic level in a category hierarchy but not the category hierarchy that people actually use in everyday life  Not just IS-A relationship – messier – more like ontologies  Examples: – Scenes – indoors – school – elementary school – Events – travel – highway travel – truck travel – Emotions – positive emotion – joy – contentment – Programming – Algorithm – sort – binary

11 11 Basic Level Categories Introduction: Other levels  Subordinate – more informative but less distinctive – Basic shape and function with additional details Ex – Chair – office chair, armchair – Convention – people name objects by their basic category label, unless extra information in subordinate is useful  Superordinate – Less informative but more distinctive – All refer to varied collections – furniture – Often mass nouns, not count nouns – List abstract / functional properties – Very hard for children to learn

12 12 Basic Level Categories Introduction: How recognize Basic level  Short words – noun phrase – Selected list (extended stop words)  Kinds of attributes – Superordinate – functional (keeps you warm, sit on it) – Basic – Noun and adjectives – legs, belt loops, cloth – Subordinate – adjectives – blue, tall  Basic Level – similar movements, similar shapes  More complex for non-object domains  Issue – what is basic level is context dependent

13 13 Basic Level Categories Introduction: How recognize Basic level  Cue Validity – probability that a particular object belongs to some category given that it has a particular feature (cue) – X has wings – bird – Superordinates have lower – fewer common attributes – Subordinates have lower – share more attributes with other members at same level  Category utility – frequency of a category + category validity + base rates of each of these features  Issue – how decide which features? – Cat – “can be picked up”, is bigger than a beetle

14 14 Basic Level Categories and Expertise  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

15 15 Basic Level Categories and Expertise  Experts chunk series of actions, ideas, etc. – Novice – high level only – Intermediate – steps in the series – Expert – special language – based on deep connections  Expertise is a combination of knowledge and skill – Everything from riding a bike to merging two companies – No such thing as tacit knowledge - spectrum  Types of expert : – Technical – lower level terms only – Strategic – high level and lower level terms, special language

16 16 Basic Level Categories Analytical Techniques  What is basic level is context(s) dependent  Documents / Tags – analyze in terms of levels of words – Taxonomy for high level – Length for basic – short – Length for subordinate – long, special vocabulary  Category Utility  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

17 17 Basic Level Categories Analytical Techniques  Corpus context dependent – Author748 – is general in scientific health care context, advanced in news health care context  Need to generate overall expertise level for a corpus  Also contextual rules – “Tests” is general, high level – “Predictive value of tests” is lower, more expert  Categorization rule – SENT, DIST – If same sentence, expert  Demo – Sample Documents, Rules

18 18 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

19 19 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

20 20 Basic Level Categories Expertise – application areas  Taxonomy 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 – ID communities by their documents, tags  Info presentation – combine superordinate and basic – Similar to scientific – Genus – Species is official name  Info presentation – document maps – expose basic level

21 21 Basic Level Categories Expertise – application areas  Ontology development / design – Need more focus on who is intended audience Structure, nomenclature – Defining classes & hierarchy – same as taxonomy – Defining properties - Expert dependent Wine for snobs (experts) very different than Joe Sixpack – Two approaches One ontology, classes and/or properties as expert Two ontologies – expert and novice

22 22 Basic Level Categories Expertise – application areas  Text Mining – Preprocessing of documents – Expertise characterization of writer – Best results with existing taxonomy Can use a very general, high level taxonomy – superordinate and basic Can use existing large taxonomies – MeSH, etc.  eCommerce – Organization and Presentation of information – expert, novice – How determine? Search queries, profiles, buying patterns, specific products

23 23 Basic Level Categories Expertise – application areas  Search – enterprise and/or internet – Query level  Relevance ranking – Adjust documents for novice and expert queries  Information presentation – Tag clouds – match novice and expert  Clustering – Incorporate into clustering algorithms – Presentation – expose basic level & provide up and down browse

24 24 Basic Level Categories Expertise – 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  Issue of Levels of expertise – how granular?

25 Basic Level Categories Expertise – 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 25

26 Basic Level Categories Expertise – Related Tags - Delicious  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 26

27 27 Basic Level Categories Expertise – 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

28 28 Basic Level Categories Conclusions  Basic Level Categories are fundamental to thought  What is basic level is context dependent  Basic level effect is most obvious with objects, more work for concepts  Most domains need some taxonomy – need not be big – Categorization-like rules  This is exciting, but not a revolution  Beware Egalitarian stance – People are different  Text Analytics needs Cognitive Science – Not just library science or data modeling or ontology

29 29 Resources  Books – Women, Fire, and Dangerous Things George Lakoff – Knowledge, Concepts, and Categories Koen Lamberts and David Shanks – The Stuff of Thought – Steven Pinker  Web Sites – Text Analytics News - http://social.textanalyticsnews.com/index.php http://social.textanalyticsnews.com/index.php – Text Analytics Wiki - http://textanalytics.wikidot.com/http://textanalytics.wikidot.com/

30 30 Resources  Blogs – SAS- Manya Mayes – Chief Strategist - 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/ – 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 – coming soon

31 31 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

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


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