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Improving Navigation and Findability

Improving Navigation and Findability. Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com. Agenda. Introduction Semantics, Taxonomy, and Faceted Navigation Key Ideas Review of Media Sites Key Elements – Common Themes

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Improving Navigation and Findability

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  1. Improving Navigation and Findability Tom ReamyChief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. Sample eCommerce Sites • Pure Facets – Product Catalogs • Library Catalogs • Traditional Search • Search and Categories • Facets, Taxonomies, and Semantics,

  11. Three Environments: E-Commerce

  12. Three Environments: E-Commerce

  13. 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?

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. Questions? Tom Reamytomr@kapsgroup.com KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

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