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Taxonomy & Ontology Impact on Search Infrastructure

Explore the impact of taxonomy and ontology on search infrastructure, including context analysis, rich media integration, and adaptive information warehouses. Learn about structured data processing, content refinement, and the role of human factors in search architecture.

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Taxonomy & Ontology Impact on Search Infrastructure

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  1. Taxonomy & Ontology Impact on Search Infrastructure John R. McGrath Sr. Director, Fast Search & Transfer

  2. Next Generation Search • 360º View of Enterprise • Hyper-personalization • Contextual synergy • Technology transparency • Central governance • Extended Search • Extended platform (desktop, mobile) • Intelligent use of context (Web, geospatial) • rich media integration Information Management Growth Market Premium Content Delivery Compliance and Content Governance Enterprise eCommerce Knowledge Management Market Potential • Conventional Search • search bar w/ results • fixed relevancy model • static navigation • few data sources Detection, Surveillance and Enforcement Business Intelligence • Predictive Search • alerting through business rules • tracking and monitoring • high order analytics • pattern matching • Advanced Search • tunable relevancy and navigation • wide array of sources (structured, unstructured) • static navigation eTailing Mature Market General Site Search, Intranet Conventional Innovative Search Innovation The Real Search Solution Space Evolving more powerful capability

  3. Improving Search through Contextual AnalysisThe Importance of Context in Search Queries Usage Patterns Location Interest Profiles Business Rules Metadata Editorial Control Program Control Organization Statistics

  4. When was D-day? Improving Search through Conceptual AnalysisEmbedded Application Semantics FAST Answers Rich Media Extreme precision applications: • Self-service, NLP, Mobile, Compliance • Information discovery, Intelligence • Web 2.0 – The semantic web • Scene level discovery • Podcasts: Extreme precision access The Adaptive Information Warehouse • Visualize implicit facts • Visualize uncertainty • Use embedded semantics: • Ex: Patent claims • Ex: Blogs • Linguistic cleansing • On-the-fly fact mining • 10-200 X query speed-ups • Low latency access to extreme volumes

  5. SEARCH Applying Ontology to the Search Architecture App. Logic Tagging Extraction Classification Relationship Personalization Term Expansion Query Control Source Selection Refinement Logic STRUCTURED DATA SEARCH QUERY PROCESSING CONTENTREFINEMENT UNSTRUCTURED DATA ALERT RESULT PROCESSING Presentation Security App logic Navigation RICH MEDIA SEARCH & ADMINISTRATION MANAGEMENT

  6. Primary applications today in search Classify documents – facilitate simple, keyword-based retrieval Provide a common language, or thesaurus – offer terms to refine a search from a consistent, controlled vocabulary Create browse-able directories – facilitate rapid navigation through defined hierarchies of information Promote meaningful clustering – establish ‘fixed points’ for clustering results Generate pick-list elements – select or combine terms to limit/define your search domain Expedite query refinement – refine/exclude on similarly tagged items

  7. FAST Relevancy Framework Multiple levels of control Accessible to… Control Mechanisms Application Model End Users • Sorting • Navigation • Feedback Business Rules Business Managers • Alert Parameters • Page “boosting” Levels of control User Profiles Administrator • Rank Profile • Concept Security Developer • Dynamic Algorithm “weights” Core Algorithmic Model

  8. Human Factors Considerations Limited user capacitance • Most business users do not navigate deeper than 4 levels in a taxonomy • More than 10 choices/nodes per level impacts willingness to move deeper, to next level Multiple perceptions of value • Provides navigation for discovery (40%) • Organizes disparate info (18%) • Structures KM repository (16%) • Automates classification & alerting (14%) • Enhances searching(12%) Distribution of expected benefits • Increased productivity (21%) • Reduced search time ( 20% ) • Increased knowledge sharing (18%) • Shortened time to decision (16%) • Improved collaboration (13%) • Discover new opportunities (10%) Source : Delphi Group 2004 Survey – 300 respondents

  9. Observed Trends Taxonomies and ontologies are expense to develop and maintain. • Published works and services • Social network product • Automatic generation

  10. Observed Trends Primary application is “smart” navigation. • Simple knowledge bases • Supervised clustering

  11. Near Future? Application of Knowledge bases is increasing to support advanced features. • Extraction and association • Relationship analysis • Advanced personalization

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