Henry stewart dam2010_taxonomicsearch_markohurst
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    Henry stewart dam2010_taxonomicsearch_markohurst Henry stewart dam2010_taxonomicsearch_markohurst Presentation Transcript

    • Leveraging Taxonomy & Metadata For Superior Search Relevancy
      Achieve greater search relevancy with content structure
      Speaker: Marko Hurst
    • Me
      Consultant, Author, & Speaker
      User Experience / Experience Design
      Web Analytics
      14 years experience
      Search Systems, Data Analysis, Enterprise Applications, Websites, Mobile, Web. 2.0
      Independent, agency, & consulting firms
      National & regional lead for UX, Strategy, & Web Analytics practices
      Industries: Government, Media, eCommerce, Financial Services, Automotive, Technology, Mobile, CPG
      MISI, Engagement Manager:mhurst@misicompany.com
      Read my Blog:MarkoHurst.com“Insightful Analytics”
      Follow me on Twitter:MarkoHurst
    • Me
      Book: Search Analytics - Conversations With Your Customers
      Anticipated release: late 2010
      Book website:RosenfeldMedia.com/books/SearchAnalytics
      Co-Author: Lou Rosenfeld
      North America & Europe
      eMetrics Marketing Optimization Summit
      Search Marketing Exchange, SMX
      Usability Professional Association, UPA
      Digital Asset Management
      Government Agencies
    • Before We Begin
      Audience Survey
    • Audience Survey
      Question 1
      Who uses only an algorithm (does not leverage a formal taxonomy or metadata structure) for search results?
      It’s OK, I’m here to help
    • Audience Survey
      Question 2
      Who uses a taxonomy to aid search relevancy?
      Sweet! – You’re ahead of most
    • Audience Survey
      Question 3
      Who uses a ontology to aid search relevancy?
      I salute you! You will find Nirvana here.
    • What We’re Going To Cover
      Definition of Common Terms
      Taxonomy and Search
      Metadata and Search
      Ontology and Search
    • Definitions
      Let’s be sure we’re talking the same language
    • Definition: Taxonomy
      A parent / child hierarchal relationship between two or more items
      Knowledge map that allows users to access relevant objects, ideas and/or experts quickly and efficiently
      Taxonomies classify domains of knowledge and show the hierarchical relationships between categories, sub-categories and values within categories
      Geek Speak
      Taxonomy is the practice and science of classification and comes from the Greek “taxis” – “order" (or arrangement or division) and "nomos", meaning law or science
      Taxonomies, which are composed of taxonomic units known as taxa (singular taxon), are frequently hierarchical in structure, commonly displaying parent-child relationships
    • Taxonomy: Sample Screenshot - DAM System
    • Taxonomy: Conceptual
    • Definition: Metadata
      “Data about data” of any sort in any media (paper-based or electronic media).
      Metadata describes how and when and by whom a particular asset was collected, and how the asset is formatted asset in order to provide access to the asset
      Metadata is text, voice, or image that describes what the audience wants or needs to see or experience
      Geek Speak
      In its broadest sense, “metadata” can be used to describe information structures
      Metadata is a summary of the form and content of a resource, i.e. books: titles, authors, publishers, ISBN, etc.
      Usually includes information about the intellectual content of the image, digital representation data, and security or rights management information
    • Metadata: DAM System Assigning Metadata
    • Definition: Ontology
      Form associative relationships between two or more items
      Metadata describes how and when and by whom a particular asset was collected, and how the asset is formatted asset in order to provide access to the asset
      an explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them and describes rather than the hierarchy, the relationship between entities
      Geek Speak
      Ontologies resemble faceted taxonomies but use richer semantic relationships among terms and attributes, as well as strict rules about how to specify terms and relationships
      Because ontologies do more than just control a vocabulary, they are thought of as knowledge representation
      Ontologies can represent complex relationships between objects, and include the rules and axioms missing from semantic networks
    • Ontology: Protégé
    • Ontology: Conceptual
    • Ontology: Visual Word Cloud
      Associative relationships for “Legacy Loan Program”
    • Taxonomic Search
      Putting content structure to work
    • Surfacing Content With Search
      Two dimensions for surfacing content within search: Semantic and Taxonomic
      Semantic / Text paradigm
      Pertains to search only
      Search (pull) relies upon textual matching and semantic algorithms to surface relevant content
      Search engine derives semantics from phrases and words in unstructured content and from field-definition in structured content
      Taxonomic paradigm
      Pertains to search (pull), personalization, and customization (push)
      Utilizes a taxonomy to surface relevant content.
      Search interrogates taxonomy and ontology (associative relationships)
      Be aware
      Both approaches have advantages and disadvantages
      Both approaches have significant challenges – there are NO easy options!
      Possible to incorporate both approaches in a single search solution in effect creating two concurrent searches
    • Semantic vs. Taxonomy Based Search
      Semantic/Free Text Search
      Searches against the content of a database repository (i.e. involves only two steps: search  contents)
      Uses only the keyword(s) entered into the search engine
      Where these is an exact match, a result is returned
      This gives results that are less expansive, less controlled and often, less relevant...
    • Semantic vs. Taxonomy Based Search
      Taxonomy Based Search
      Searches against the metadata associated with the content stored in a database repository (i.e. involves three steps: search  metadata  contents)
      The metadata (NOT the search itself) is mapped against the contents of a database repository
      Can match the user’s search word entered into the search engine with synonyms mapped in the taxonomy
      (e.g. “"P.D. 533" maps to "Presidential Decree No. 533” and “The anti-Cattle Rustling Law of 1974”).
      Matches can be made between a user's query and related terms mapped in the taxonomy,
      e.g. “P.D. 533” might be mapped to "Cattle Theft"; "Felony" to "Legislation”)
    • Semantic vs. Taxonomy Based Search
      Taxonomy Based Search (con’t)
      Search results can be prioritized and categorized by filtering for pages and/or documents associated with specific search terms
      e.g. "best bets”
      When there is ambiguity, can ask users to refine their searches by providing “Did you mean...?” feedback
      This gives results that are more expansive and relevant…
    • How Taxonomy Aids Searching
      A taxonomy aids searching by…
      Restricting searches within a finite category or set of categories
      e.g. a search for “Farm Bill" will be restricted to the category "Legislation”
      Expanding searches to higher (parent), lower (e.g. child) or equivalent (e.g. sibling) categories
      e.g. a search for “"Cattle Poaching" would search across "Legislation", "PB 553", “SB 1163” "Felony" “Products”, etc.
    • Restricting / Expanding Search With Taxonomy
      Parent node
      Sibling node
      Search for “Farm Bill”
      Child node
      Child node
    • How Taxonomy Aids Searching (con’t)
      Provide “Did you mean?” feedback to users to refine searches
      e.g. a search for “Livestock” might return: “Did you mean Livestock Health, Livestock Management, Livestock Legislation or Supplies & Equipment?”
      Search against synonyms (i.e. alternate terms)
      e.g. a query against the acronym “DDT" would map to “dichlorodiphenyltrichloroethane”, and search in the "Pesticides" category
      Search against related terms
      e.g. “Pesticide" is a term that exists in both "Crop Plants" and "Products”
      Because this relationship is known (and mapped) in the taxonomy, searches on one usage will also return "hits" on the other
      Search on obscure or obtuse relationships
      e.g. Paul Hermann Müller, Rachel Carson, environmental movement, and the book Silent Spring can all be mapped
    • How Taxonomy Aids Searching (con’t)
      Allows for easier auto-complete / type ahead functionality
      Serves as a short‐cut
      Helps users to avoid unnecessary typing
      Assists with spelling
      May suggest related or more specific queries (that begin with or include that word or phrase
    • Taxonomic Search
    • Summary
      Two dimensions for surfacing content within search: Semantic and Taxonomic
      Semantic is pull only
      Taxonomic is push & pull
      Both have pros & cons
      The best search results can typically be achieved by using both
      Taxonomy is a parent / child relationship between two or more items
      Taxonomic search
      Allows for use of synonyms & mapping to related & obscure relationships
      Allows for expanding and restricting of content by moving up (parent), moving across (sibling), or moving down (child) nodes within the taxonomy
      Many benefits / features can be used within the interface using taxonomy
      Did You Mean?, Auto-Suggest, Best Bets, etc.
      Metadata is “data about data” of any sort in any media
      Taxonomies provide an inherent level of metadata that is not possible otherwise
      Leveraging metadata frameworks (Dublin Core, PRISM, etc) allow for standard methods of
      Associative relationship between two or more items
      Synonyms, controlled vocabularies, metadata, etc can be mapped taxonomy items for greater expansion & contraction of related content
    • Thank You!
      Contact: mhurst@misicompany.com
      Book: RosenfeldMedia.com/books/SearchAnalytics
      Blog: MarkoHurst.com
      Twitter: MarkoHurst