Taxonomy DevelopmentKnowledge Structures

Tom ReamyChief Knowledge Architect

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com

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Agenda

Introduction

Knowledge Structures

Taxonomy Management Software

Exercises

Conclusion

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Knowledge Structures

List of Keywords (Folksonomies) Controlled Vocabularies, Glossaries Thesaurus Browse Taxonomies (Classification) Formal Taxonomies Faceted Classifications Semantic Networks / Ontologies Topic Maps Knowledge Maps

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Knowledge StructuresLists of Keywords (Folksonomies)

Folksonomy (also known as collaborative tagging, social classification, social indexing, and social tagging) is the practice and method of collaboratively creating and managing tags to annotate and categorize content. Folksonomy describes the bottom-up classification systems that emerge from social tagging.[1] In contrast to traditional subject indexing, metadata is generated not only by experts but also by creators and consumers of the content. Usually, freely chosen keywords are used instead of a controlled vocabulary.[2] Folksonomy (from folk + taxonomy) is a user generated taxonomy.

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Knowledge StructuresControlled Vocabularies, Glossaries

Controlled Vocabularies, Glossaries– Lists with minimum structure– Easy to develop– Difficult to get value from– Simple Reference resource

Thesaurus– Taxonomy-like – Less formal– BT, NT – also RT

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Two Types of Taxonomies: Browse and Formal

Browse Taxonomy – Yahoo

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Two Types of Taxonomies: Formal

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Facets and Dynamic Classification

Facets are not categories– Entities or concepts belong to a category– Entities have facets

Facets are metadata - properties or attributes– Entities or concepts fit into one category– All entities have all facets – defined by set of values

Facets are orthogonal – mutually exclusive – dimensions– An event is not a person is not a document is not a place.

Facets – variety – of units, of structure– Date or price – numerical range– Location – big to small (partonomy)– Winery – alphabetical– Hierarchical - taxonomic

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Knowledge StructuresSemantic Networks / Ontologies

Ontology more formal XML standards – OWL, DAML Semantic Web – machine understanding RDF – Noun – Verb – Object

– Vice President is Officer Build implications – from properties of Officer Semantic Network – less formal

– Represent large ontologies– Synonyms and variety of relationships

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Knowledge Structures: Ontology

Music Instruments

ViolinsBluegrass

Violinists

Musicians

uses

uses

is ais a

create

is a

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Knowledge StructuresTopic Maps

ISO Standard See www.topicmaps.org Topic Maps represent subjects (topics) and associations

and occurrences Similar to semantic networks Ontology defines the types of subjects and types of

relationships Combination of semantic network and other formal

structures (taxonomy or ontology)

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Knowledge Structure: Topic Maps

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Knowledge StructuresKnowledge Maps

No standards – applied at high level Ontologies plus / applied to specific environment Map of Groups – Content Stores – Purpose – Technology Add structure to each element Facet Structure – filter by group – content – purpose Strategic resource

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Knowledge Structures: Which one to use?

Level 1 – keywords, glossaries, acronym lists, search logs– Resources, inputs into upper levels

Level 2 – Thesaurus, Taxonomies– Semantic Resource – foundation for applications, metadata

Level 3 – Facets, Ontologies, semantic networks, topic maps

– Applications

Level 4 – Knowledge maps – Strategic Resource

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Web 2.0 – No need for Taxonomies etc.?

“Tags are great because you throw caution to the wind, forget about whittling down everything into a distinct set of categories and instead let folks loose categorizing their own stuff on their own terms." - Matt Haughey - MetaFilter

Tyranny of the majority - worst type of central authority More Madness of Crowds than Wisdom of Crowds “Things fall apart; the center cannot hold;

Mere anarchy is loosed upon the world,…The best lack all conviction, while the worstAre full of passionate conviction.” - The Second Coming – W.B. Yeats

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Advantages of Folksonomies

Simple (no complex structure to learn)– No need to learn difficult formal classification system

Lower cost of categorization– Distributes cost of tagging over large population

Open ended – can respond quickly to changes Relevance – User’s own terms Support serendipitous form of browsing Easy to tag any object – photo, document, bookmark Better than no tags at all Getting people excited about metadata!

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Folksonomies – Problems and Limits

Folksonomies don’t compare with taxonomies or ontologies Serendipity browsing is small part of search Limited areas of success – popular sites are popular Quality Content – finance, science, etc – not good candidates No mechanism for improving folksonomies Scale – Too Big (million hits) – Too Little (200 items) – Amazon

and LibraryThing Need intrinsic value of tagging – not tagging for better tags Bad Tags - idiosyncratic or too broad, errors, limited reach

– Most people can’t tag very well – learned skill

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Del.icio.us Tags

Design blog software music tools reference art video programming webdesign web2.0 mac howto linux tutorial web free news photography shopping blogs css imported education travel javascript food games

Development inspiration politics flash apple tips java google osx business windows iphone science productivity books toread helath funny internet wordpress ajax ruby research humor fun technology search opensource

Photoshop media recipes cool work article marketing security mobile jobs rails lifehacks tutorials resources php social download diy ubuntu freeware portfolio photo movies writing graphics youtube audio online

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Del.icio.us - Folksonomy Findability

Too many hits (where have we heard that before?)– Design – 1 Mil, software – 931,259, sex – 129,468

No plurals, stemming (singular preferred)– Folksonomy – 14,073, folksonomies – 3,843, both – 1,891– Blog-1.7M, blogs – 516,340, Weblog- 155,917, weblogs – 36,434,

blogging – 157,922, bloging – 697– Taxonomy – 9.683, taxonomies – 1,574

Personal tags – cool, fun, funny, etc– Good for social research, not finding documents or sites– How good for personal use? Funny is time dependent

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Library Thing

Book people aren’t much better at tagging High level concepts – psychology (55,000), religion

(120,000), science (101,000) Issue – variety of terms – cognitive science – need at least

40 other tags to cover the actual field of cognitive science Strange tags – book (19,000) – it’s a book site? Combination of facets and topics

– Facets – Date (16th century, 1950’s, 2007) // Function (owned, not read) // Type (graphic novel, novel) // Genre (horror, mystery)

– Topics – majority like Del.icio.us

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Library Thing – Book on Neuroscience

1) (Location: dining room)(1) biological(1) biology(8) box74(1) Brain(1) brain research(1) brains(1) cognitive neuroscience(1) cognitive science(1) consciousness(1) currently reading(1) HelixHealth(1) kognitionswissenschaft(1) medical(1) medicine(1) neuroscience(19) non-fiction(5) partread(1) Psychology(4) Science(10) textbook(10) theory(1)

Too General: Science, Psychology, biology, textbook Too specific: Location: dining room, box74 Facets: currently reading, partread

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Better Folksonomies:

Will social networking make tags better? Not so far – example of Del.icio.us – same tags Quality and Popularity are very different things Most people don’t tag, don’t re-tag Study – folksonomies follow NISO guidelines – nouns, etc –

but do they actually work – see analysis Most tags deal with computers and are created by people

that love to do this stuff – not regular users and infrequent users – Beware true believers!

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Browse Taxonomies: Strengths and Weaknesses Strengths: Browse is better than search

– Context and discovery– Browse by task, type, etc.

Weaknesses:– Mix of organization

• Catalogs, alphabetical listings, inventories• Subject matter, functional, publisher,

document type– Vocabulary and nomenclature Issues– Problems with maintenance, new material– Poor granularity and little relationship

between parts.• Web site unit of organization

– No foundation for standards

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Formal Taxonomies: Strengths and Weaknesses

Strengths:– Fixed Resource – little or no maintenance– Communication Platform – share ideas, standards– Infrastructure Resource

• Controlled vocabulary and keywords

• More depth, finer granularity

Weaknesses:– Difficult to develop and customize– Don’t reflect users’ perspectives

• Users have to adapt to language

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Faceted Navigation: Strengths and Weaknesses

Strengths:– More intuitive – easy to guess what is behind each door

• 20 questions – we know and use– Dynamic selection of categories

• Allow multiple perspectives– Trick Users into “using” Advanced Search

• wine where color = red, price = x-y, etc.. Weaknesses:

– Difficulty of expressing complex relationships • Simplicity of internal organization

– Loss of Browse Context• Difficult to grasp scope and relationships

– Limited Domain Applicability – type and size• Entities not concepts, documents, web sites

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Dynamic Classification / Faceted navigation Search and browse better than either alone

– Categorized search – context– Browse as an advanced search

Dynamic search and browse is best– Can’t predict all the ways people think

• Advanced cognitive differences• Panda, Monkey, Banana

– Can’t predict all the questions and activities• Intersections of what users are looking for

and what documents are often about• China and Biotech• Economics and Regulatory

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Varieties of Taxonomy/ Text Analytics Software

Taxonomy Management Text Analytics

– Auto-Categorization, Entity Extraction– Sentiment Analysis

Software Platforms – Content Management, Search

Application Specific– Business Intelligence

Vendors of Taxonomy/ Text Analytics Software

Attensity Business Objects –

Inxight Clarabridge ClearForest Data Harmony / Access

Innovations

Lexalytics Multi-Tes Nstein SchemaLogic Teragram Wikionomy Wordmap Lots More

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Why Taxonomy Software?

If you have to ask, you can’t afford it Spreadsheets

– Good for calculations, days of taxonomy development over– (almost)

Ease of use – more productive– Increase speed of taxonomy development– Better Quality – synonyms, related terms, etc.

Distributed development – lower cost, user input (good and bad)

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Text Analytics Software – Features

Taxonomy Management Functions Entity Extraction

– Multiple types, custom classes Auto-categorization – Taxonomy Structure

– Training sets – Bayesian, Vector space– Terms – literal strings, stemming, dictionary of related terms– Rules – simple – position in text (Title, body, url)– Boolean– Full search syntax – AND, OR, NOT– Advanced – NEAR (#), PARAGRAPH, SENTENCE

Advanced Features– Facts / ontologies /Semantic Web – RDF +– Sentiment Analysis

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Conclusion

Variety of information and knowledge structures– Important to know what will solve what

Taxonomies and Facets are foundation elements Build higher levels based on lower levels

– Glossaries to Taxonomies– Taxonomy to Ontology / faceted navigation

Important to have good taxonomy and text analytics software (spreadsheets are OK for first draft)

Web 2.0/Folksonomies are not the answer

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Resources

Books– Women, Fire, and Dangerous Things

• George Lakoff– Knowledge, Concepts, and Categories

• Koen Lamberts and David Shanks– The Stuff of Thought – Steven Pinker

Software– Tools & Techniques (Taxonomy Boot Camp)

Web Sites – Taxonomy Community of Practice:

http://finance.groups.yahoo.com/group/TaxoCoP/

Questions?

Tom Reamytomr@kapsgroup.com

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com