Improving Navigation and Findability Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com
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Improving Navigation and Findability
Tom ReamyChief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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Agenda
Introduction Semantics, Taxonomy, and Faceted Navigation Key Ideas Review of Media Sites
– Key Elements – Common Themes– What Works and What doesn’t
Development Guide – Semantics and Faceted Navigation Conclusion
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KAPS Group: General
Knowledge Architecture Professional Services Virtual Company: Network of consultants – 12-15 Partners – Business Objects SA, Endeca, Interwoven, 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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Semantics and Facets: Key IdeasReal Key – All of the above Facet – orthogonal dimension of metadata Taxonomy - Subject matter / aboutness Ontology – Relationships / Facts
– Subject – Verb - Object Software - Text analytics, auto-categorization People – tagging, evaluating tags, fine tune rules and
taxonomy, social tagging, suggestions
Enterprise Search Summit Sourcebook 2008-2009– A Knowledge Architecture Approach to Search
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Essentials of Facets
Facets are not categories– Categories are what a document is about – limited number– Facets are types of metadata attributes
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– Numerical range (price), Location – big to small– Alphabetical, Hierarchical – taxonomic
Facets are designed to be used in combination• Wine where color = red, price = excessive, location = Calirfornia,• And sentiment = snotty
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Advantages of Faceted Navigation
More intuitive – easy to guess what is behind each door• Simplicity of internal organization• 20 questions – we know and use
Dynamic selection of categories• Allow multiple perspectives
Systematic Advantages – fewer elements– 4 facets of 10 nodes = 10,000 node taxonomy– Ability to Handle Compound Subjects
Flexible – can be combined with other navigation elements
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Essentials of Taxonomies
Formal Taxonomy – parent – child relationship– Is-A-Kind-Of ---- Animal – Mammal – Zebra – Partonomy – Is-A-Part-Of ---- US-California-Oakland
Browse Classification – cluster of related concepts– Food and Dining – Catering – Restaurants
Taxonomies deal with semantics & documents– Multiple meanings and purposes– Essential attributes of documents are not single value
Taxonomies combined with facets – Supports an essential way of thinking– Can get value with smaller taxonomies– Formal taxonomies tend to work better
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Essentials of Ontologies
Facts– Subject – Verb – Object– Fred isa Vice-President
Relationships – Vice-Presidents - Have Employees & Bosses
Implications• Vice-Presidents - Make more than managers
Knowledge Representation– XML, RDF / OWL / Inference Rules
Knowledge Based Reasoning Applications Technology in search of a business model
– Knowledge is really hard
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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
• Panda, Monkey, Banana– Can’t predict all the questions and activities
• China and Biotech• Economics and Regulatory
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Sample eCommerce Sites
Pure Facets – Product Catalogs– Library Catalogs
Traditional Search
Search and Categories
Facets, Taxonomies, and Semantics,
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Three Environments: E-Commerce
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Three Environments: E-Commerce
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eCommerce Common Themes
Balance of commerce and information Source and Type are basics Standard Facets – People, Companies, Place, Industry Interactive interface – sliders, date ranges Taxonomy – just another facet?
– Keywords vs. simple taxonomy Semantics still hardest – summaries, related, rank Tag Clouds / Clusters – how useful?
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eCommerce: Issues
Balance of information and ads– Advertiser dominance – No– Auto-ads – Obituary for Obama
1 or 2 filters (source / type) – No– Intersection of facets is source of power
Facets not orthogonal – topics and issues Good Information Architecture
– Space wars – summary or full facet display– Simplicity vs. research power
Integrated design – Complex, not complicated
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Integrated Design – Facets & SemanticsDesign 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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Semantics and Facets: DevelopmentElements – More Metadata! Text Analytics Software
– Entity / Noun Phrase – metadata value of a facet• feeds facets, signature, ontologies
– Taxonomy and categorization rules• Auto-categorization – feeds subject facets
Variation of eCommerce and Enterprise– When and how add metadata, additional facets– CM – Hybrid of taggers, software, and policy– Software offers suggested categorization, facet values– Relevance – best bets to ontology based relevance
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Semantics and Facets: Development Software Tools – Auto-categorization Auto-categorization
– Training sets – Bayesian, Vector Machine– 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 +– Sentiment Analysis – positive, negative, neutral
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Semantics and Facets: Development Software Tools – Entity Extraction Dictionaries – variety of entities, coverage, specialty
– Cost of update – service or in-house– Inxight – 50+ predefined entity types– Nstein – 800,000 people, 700,000 locations, 400,000 organizations
Rules– Capitalization, text – Mr., Inc.– Advanced – proximity and frequency of actions, associations– Need people to continually refine the rules
Entities and Categorization– Total number and pattern of entities = a type of aboutness of
the document – Bar Code, Fingerprint
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Conclusions
Documents – more complicated than products, later start– Need facets plus taxonomies, semantics
Integrated design is essential – not facets as add on Semantics is still not there – hardest, but some progress Text Analytics (Entity extraction and auto-categorization)
are essential Future – new kinds of applications:
– Text Mining, research tools, sentiment Future of Search – smart ways to refine results, not better
relevance– Real problem with 10 mil hits – no way to get to target– Include facets, taxonomies, semantics, & lots of metadata
Questions?
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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