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