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Selecting Taxonomy Software Who, Why, How
Tom ReamyChief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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Agenda
Introduction: Basic Decision Context• What, Why, and How
Evaluating Software• Features – good, bad, and ugly
• History, Philosophy, and Evolution
Conclusion
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KAPS Group: General
Knowledge Architecture Professional Services Virtual Company: Network of consultants – 12-15 Partners – Convera, Inxight, FAST, etc. Consulting, Strategy, Knowledge architecture audit Taxonomies: Enterprise, Marketing, Insurance, etc. Services:
– Taxonomy development, consulting, customization– Technology Consulting – Search, CMS, Portals, etc.– Metadata standards and implementation– Knowledge Management: Collaboration, Expertise, e-learning– Applied Theory – Faceted taxonomies, complexity theory, natural
categories
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Varieties of Taxonomy Software
Taxonomy Management– Multi-Tes, Data Harmony, SchemaLogic
Distributed Taxonomy Development– Wordmap, Wikionomy
Text Analytics – Entity Extraction– ClearForest, Inxight, Teragram
Auto-Categorization– ClearForest, Inxight, Teragram
Embedded software – Content Management, Search
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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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Decision Points
Dedicated taxonomy management software– Small company, specialized taxonomy
Real issue is how it will be integrated Text analytics / auto-categorization
– Dedicated software or use features of CM and/or enterprise search
Combination of dedicated and embedded– Integration – export and import is critical
Integration with Policy / Procedure– Distributed contributions
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Taxonomy – How will it be used?
Browse front end to portal Search engine indexing
– Keyword searching– Hierarchical browsing – formal structure
Faceted navigation – Subject taxonomy and lots of metadata
Controlled vocabulary for entering metadata Applications – text and data mining, alerts, etc. Semantic Infrastructure
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Evaluating Taxonomy Software Historical Perspective: Four Methods
Spreadsheets were good enough for my father Flip a Coin
– 50-50 chance
Ask a Friend (Industry Recommendation)– Historical Accident?
Feature Check List and Score– Basic taxonomy functionality
Which method produces different results?
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Evaluating Taxonomy SoftwareFeature Checklist and Score: Basic Features
New, copy, rename, delete, merge – Branches not just nodes
Scope Notes Spell check Search – all parts and selected (only taxonomy nodes) Names and Identifiers for terms and nodes Versioning
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Evaluating Taxonomy SoftwareFeature Checklist and Score: Usability
Ease of use – copy, paste, rename, merge, etc. User Documentation, user manuals, on-line help, training and
tutorials Visualization
– file structure, tree– Hierarchy and alphabetical?
Automatic Taxonomy/Node Generation– Nonsense for Taxonomy– Node – suggestions – perhaps– List of terms out of context versus reading
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Evaluating Taxonomy SoftwareFeature Checklist and Score: Additional Features
Language support – international – If you have need for it
Scalability – Size of taxonomy rarely important– More important for auto-categorization
Import-Export – XML and SKOS Support standards – NISO, etc. Mapping between taxonomies API / SDK Security, Access Rights, Roles – See integration
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Evaluating Taxonomy SoftwareAdvanced Features – Taxonomy as Platform Text Analytics – multiple document types Entity Extraction
– Multiple types, custom classes Auto-categorization
– Training sets– Terms – literal strings, stemming, dictionary of related terms– Rules – simple – position in text (Title, body, url)– Advanced – saved search queries (full search syntax)– NEAR, SENTENCE, PARAGRAPH– Boolean – X NEAR Y and Not-Z
Advanced Features– Facts / ontologies /Semantic Web – RDF +
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Evaluating Taxonomy Software “Philosophy” Perspective
Self-Knowledge is the highest form of knowledge. It’s not what you do, it’s who you know.
– Importance of who on team
Life is meaningless and absurd– And so are most search/categorization results
Beauty and Meaning are in the eye of the beholder– Raise your hand if you think I’m more beautiful than …
“The real constitution of things is accustomed to hide itself”– Beware 2.0 “solutions”
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Self Knowledge is the highest form of knowledge
Start with self knowledge – KA audit – content, users, technology, business and information behaviors
Develop a model of taxonomy use in your enterprise Ask Experts – Taxonomy is not for faint of heart If test – use own content
– Balance of current application and platform– Use the test to get a head start on taxonomy development
Spend more time on self knowledge than vendor capability.
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Evaluating Taxonomy SoftwareSelf Knowledge – Distributed model of taxonomy in action People
– Interdisciplinary Team– Knowledge architects, editors, SME, users
Roles– Select and implement taxonomy software, input into CM, Search– Care and feeding of taxonomies, metadata, vocabularies– Initial filter of user input, monitoring user input, answer questions– Provide input – what works and not, new terms
Technology– Develop taxonomies, vocabularies, facets– Integrate taxonomy into CM, search, applications
Activities– Information needs and behaviors – support with advanced features
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It’s not what you know, it’s who you knowDesign of the Taxonomy Selection Team
Traditional Candidates - IT Experience with large software purchases
– Search/Categorization is unlike other software
Experience with needs assessments– Need more – know what questions to ask, knowledge audit
Objective criteria– Looking where there is light?– Asking IT to select taxonomy software is like asking a construction
company to select the design of your house.
They have the budget– OK, they can play.
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It’s not what you know, it’s who you knowDesign of the Taxonomy Selection Team
Traditional Candidates - Business Owners Understand the business
– But don’t understand information behavior
Focus on business value, not technology– Focus on semantics is needed
They can get executive sponsorship, support, and budget.– OK, they can play
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It’s not what you know, it’s who you knowDesign of the Taxonomy Selection Team
Traditional Candidates - Library Understand information structure
– But not how it is used in the business
Experts in search experience and categorization– Suitable for experts, not regular users
Experience with variety of search engines, taxonomy software, integration issues
– OK, they can play
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It’s not what you know, it’s who you knowDesign of the Taxonomy Selection Team
Interdisciplinary Team, headed by Information Professionals
Relative Contributions– IT – Set necessary conditions, support tests– Business – provide input into requirements, support project– Library – provide input into requirements, add understanding
of search semantics and functionality
IP – Rank the relative contributions– Knowledge Audit – understand information behaviors– Taxonomy in full context
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Evaluating Taxonomy SoftwareEvolutionary Approach Eliminate the unfit
– Filter One- Ask Experts - reputation, research – Gartner, etc.• Market strength of vendor, platforms, etc.• Look for minimum features,
– Filter Two – Technology Filter – match to your overall scope and capabilities – Filter not a focus
– Filter Three – Focus Group one day visit – 3-4 vendors– Filter Four – deep pilot (2) – advanced, integration
Evolve higher life forms– Focus on working relationship with vendor.– Focus on ease of customization
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Conclusion
Start with self-knowledge Taxonomy is not an end it itself – what will you use it for? Basic Features are only filters, not scores Integration – need an integrated team (IT, Business, KA) Integration – right balance, location (dedicated or
embedded) Integration – Distributed model of taxonomy development
and applications– Central team and distributed authors, users– CM, Sharepoint, Search, Advanced Applications
Questions?
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
