Metadata and Taxonomies The Best of Both Worlds Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services

2 Agenda  Taxonomy Good, Metadata Bad – To Metadata or not to Metadata – Issues and Approaches to Metadata  Taxonomies, Browse, Facets, and Metadata – Strengths and Weaknesses – Uses and Value of Each  Knowledge Architecture Solutions – Putting the Pieces Together: Why, Who, How – Deep Personalization and Other Advanced Applications  Conclusion – How do I get there from here?

3 Metadata about Metadata: Two Sources  Global Corporate Circle DCMI 2003 Workshop – Importance of Metadata – Difficulty of implementation and justification  KAPS Group Experience – Consulting, Taxonomy & Metadata, Strategy – Knowledge architecture audit – Partners – Inxight, Convera, etc. – Intellectual infrastructure for organizations Knowledge organization, technology, people and processes Search, CM, portals, collaboration, KM, e-learning, etc  EContent October Article – To Metadata or not to Metadata

4 Taxonomy Good, Metadata Bad  To Metadata or not to Metadata  That is the Question  Whether ‘tis nobler in the mind to suffer the slings and arrows of outrageous search results  Or to take up metadata against a sea of irrelevance  And by organizing them find them?

5 To Metadata or not to Metadata?  Why Not Metadata? – Costly - $200K to set up, maintenance costs – Difficult to do Missing, incorrect, confusing, inconsistent Poor quality metadata can make search worse  Why Metadata? – Not doing Metadata is more expensive $8,200 an employee a year – Ways to lower the cost – not all custom jobs – Need more sophisticated ROI – stories, business needs, requirements

6 Metadata Approaches: 4 Not So Good Alternatives  Metadata, we don’t need no stinking metadata – Condemned to wander search results lists forever – Need to answer these people  KA Team – Consultants – Costly, Still need to maintain  Automatic metadata (clustering & categorization) – Uneven, poor quality  Author generated metadata – Uneven quality, inconsistent – Cultural – getting authors to want to do it

7 Knowledge Architecture Solutions: The Right Context  No one solution – Can’t answer content questions from perspective of content alone – need to understand users and activities and organization  Context – understanding your context – Match amount of metadata to value – Match type of metadata to content and use – Lower the cost and increase the value  The problem is not that metadata initiatives have been too complex, it’s been that they have been too simple. – Metadata is more than adding keywords as an afterthought  For same or less effort, you can go from metadata that makes search worse to a set of solutions

8 Taxonomies, Browse, Facets, and Metadata Variety of Structures  A hierarchy does not a taxonomy make – Thesaurus (BT, NT, Related Terms), Controlled Vocabulary – Catalog, Index, site map, Partonomy, Ontology, – Classification, Semantic Network – Knowledge Map, Topic Maps, Paradigm, Prototype  4 Basic Structures – Formal Taxonomy – Aristotle & Linnaeus Concept of Species, Is-A-Kind-Of (Part) – Browse Taxonomy Yahoo – hierarchical classifications – Metadata Dublin Core – Titles, Descriptions, Keywords, + – Facets/Entities Products, Companies, People, Events, Geography

9 Taxonomies, Browse, Facets, and Metadata Four Basic Structures  Units of Organization – Taxonomy – Concepts – Browse Taxonomy – web site or content collections – Facets – Entities – Metadata – variety of values  Metadata – After or About Data – Not just documents – objects, art works, events, etc – Characteristics about the objects – Characterization of content (meaning) within object  It’s All Metadata to Me! – Browse – reverse metadata – Facets - metadata fields or sub-domains of Keywords – Taxonomy – Controlled Vocabulary

10 Taxonomies, Browse, Facets, and Metadata Strengths and Weaknesses  Formal Taxonomy Strengths – Fixed Resource - Little or no maintenance – Communication – share ideas, build on others – Infrastructure Resource Controlled vocabulary and keywords Indexing – conceptual relationships  Weaknesses – Difficult to develop and customize – Don’t reflect user’s perspective User’s have to adapt to language

11 Taxonomies, Browse, Facets, and Metadata Types of Taxonomies – Yahoo Browse

12 Taxonomies, Browse, Facets, and Metadata Strengths and Weaknesses  Browse Taxonomy Strengths – Browse better than search Context and discovery – Easiest Structure to Develop  Browse Taxonomy Weaknesses – Mix of Organization Catalogs, Alphabetical listings, Inventories – Vocabulary and Nomenclature Issues – Difficult to maintain – Poor granularity and little relationship between parts. Web Site unit of organization – No foundation for standards

13 Taxonomies, Browse, Facets, and Metadata Strengths and Weakness  Metadata Strengths – Variety of Fields supports variety of applications, user behaviors – Well developed best practices  Metadata Weaknesses – High Cost of Implementing – Inconsistent values – Studies show little value in search Have to do it completely and correctly to get any value

14 Taxonomies, Browse, Facets, and Metadata Strengths and Weakness  Facets Strengths – Orthogonal Categories – easier to understand what goes in what bin and why – Combination of formal (partonomy) and browse – Automatic Software works  Facets Weaknesses – High Cost – adding structure to facets – Can be overwhelming – 30 or more facets

15 Knowledge Architecture Solutions Metadata  Look beyond authors adding keywords to influence search results  Value from All Fields – Titles and Descriptions – balance of system and description – Publisher and author – automated and easy – DocumentObjecttype – FAQ’s, Policy Doc – supports user behavior – Audience – target information, agents – no need for search – Facets – additional fields to support multiple use

16 Knowledge Architecture Solutions Metadata  Keywords – most difficult Common terms, unique terms, aboutness terms Need to do it right and completely to get real value  Keywords - Need Taxonomy, Controlled Vocabulary – Enhance quality, consistency – Supports author generated metadata  Value from other applications – Alerts and variety of personalization schemas – Data and Text Mining – Inter-application communication  Controlled Vocabularies – Form, Format, Language, Audience, etc. – Structured – taxonomies – Multiple subjects = multiple taxonomies

17 Knowledge Architecture Solutions Metadata  Tools – Content Management, Metadata Management  People – Central – evaluate and select taxonomies Facilitate use of controlled vocabulary taxonomies Monitor and measure use of metadata and taxonomies – Authors – select from list is better, easier Automated support and work flow

18 Knowledge Architecture Solutions Taxonomies  General Intellectual Resource – Powerful Vocabulary, Glossary, Index – Standards, Naming Conventions – Communication Tool  Pre-defined Taxonomies vs. Custom Taxonomies – Pre-defined – Cross Organization Communication – Custom – specialized vocabularies – Best – Standard, Pre-defined taxonomies that are customized according to a set of established best practices  Value from Taxonomies – Indexing documents – to a very granular level – automatic – Cross application communicaiton – exchange meaning, not just bits – Dynamic Classification – structured search results Works even while advanced search does not Not Browsing

19 Knowledge Architecture Solutions Browse Taxonomies  Limited Depth (User’s set the limit) – Navigation to collections of content, web sites – Limited Content – single web site or section of web site Best for homogenous audience, common vocabulary, view  Limited Rigor – Search and Browse better than either – Broad, multiply defined categories give poor results  Combine with Facets and Taxonomies – Categories as clusters of taxonomy levels

20 Knowledge Architecture Solutions Facets  Combine Browse and Search – Structured results not advanced search – More flexible than navigation browse – Still Limited Depth – combine with classifications  Combine with Taxonomies – Added structure, especially subject areas  Selection of Facets – Ontology, Personalization  See Flamenco Project –

21 Knowledge Architecture Solutions Facets

22 Knowledge Architecture Solutions Integration: It’s All Metadata to Me!  Metadata the framework for value from Taxonomy and Facets  Metadata, Taxonomies, and Facets add value and structure to search  Taxonomy adds structure to Facets and Metadata  Facets add formal extensibility to Taxonomy  Facets add structure to Metadata and Browse Taxonomies  Integrated solution – the right mix for variety of applications

23 Knowledge Architecture Solutions: The Right Context  Content – structured & unstructured, external & internal – Publishing Policy and Procedures – Metadata, taxonomies and controlled vocabularies Standards and Best Practices  Business processes and requirements  Technologies – search, portals, CM, applications – CM is the right time for adding metadata, Automation, distributed work flow – Analytics based on meaning, not clicks – Look at the entire range of applications

24 Knowledge Architecture Solutions: People  Communities of users and information behaviors  Variety of authors, subject matter experts, publishers  Central Team supported by software and offering services – Creating, acquiring, evaluating taxonomies, metadata standards, vocabularies – Input into technology decisions and design – content management, portals, search – Socializing the benefits of metadata, creating a content culture – Evaluating metadata quality, facilitating author metadata – Analyzing the results of using metadata, how communities are using – Research metadata theory, user centric metadata – Design content value structure – more nuanced than good / poor content.

25 Knowledge Architecture Solutions: Why?  Metadata as add on to a search engine purchase will fail  Most cost effective way to produce valuable metadata  Needed to implement any alternative approach – Justification for metadata - measure and present realistic ROI – Supplement consultants – Integrate automated and author supplied metadata – Integrate content tiers into broader context  Needed for tailoring solutions to organizations

26 Knowledge Architecture Solutions: Why?  Increase the value of creating metadata – Better quality metadata Categorization experts and subject matter experts – Beyond Search and relevance ranking Dynamic classification – intersection of 2 subjects Applications – integrated metadata for portals, agents, etc – Beyond content – people metadata: Community personalization, information behaviors Community categorization  Decrease the cost of creating Metadata – Start with Standards, Distributed System and Cost

27 Knowledge Architecture Solutions: What if I can’t get there from here?  First Step – Create an infrastructure strategic vision – Including metadata standards  KA Team – can be part time, needs official recognition  Content Management is essential  Don’t start with keywords  Buy and customize taxonomies, controlled vocabularies  Relevance ranking as last resort – Best bet metadata – Browse and dynamic classifications – Faceted Displays  Think Big, Start Small, Scale Fast

Questions? Tom Reamy KAPS Group Knowledge Architecture Professional Services