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1 d.3 1 d.3 Presentation Transcript

  • When Metadata is the ContentFrom Articles to KnowledgeSSP 2009 Annual MeetingChris Beguel – Director of Sales – TEMISBaltimore, MD – May 09
  • Where are we? Semantic Age! Copyright © 2009 TEMIS –All rights reserved 2
  • From Words to Meaning… Trimilax 500 mg makes me feel dizzy after ingestionTerm Prop. Num. Abrev. Verb /3rd Pron. Verb Adj. Prep. NounEntity Product Dosing Action Target State Event ActionFact Drug Symptom Condition Potential Adverse Effect Drug = TrimilaxKnowledge Dosing = 500mg Symptom = Tireness When = After administration Copyright © 2009 TEMIS –All rights reserved 3
  • Metadata? Understand! Metadata Title: Google gives drivers a hand at the gas pumps Source: InformationWeek Author: Antone Gonsalves Date: November 7, 2007 Entities Facts Copyright © 2009 TEMIS –All rights reserved 4
  • Metadata? Understand! Metadata Entities Companies Gilbarco Veeder-Root Gilbarco Google InformationWeek T-Mobile HTC Qualcomm Motorola Persons Lucy Sackett Sackett Locations Atlanta United States Organizations National Association of Conveni… Technologies Internet Linux Open-source … Product New Service Google Service Copyright © 2009 TEMIS –All rights reserved Facts 5
  • Metadata? Understand! Who: Gilbarco Whom: unknown What: New Service Metadata When: unknown Announcement Entities Who: Gilbarco Facts What: Google Service When: early next week Announcement Gilbarco New service Who: Sackett Launch Whom: InformationWeek When: unknown Sackett InformationWeek What: unknown Launch Gilbarco Google Service Function Function Announcement Who: Gilbarco Sackett Gilbarco With whom: Google Who: Sackett When; unknown State: Negative Partnership Company: Gilbarco Who: Google Function: spoke woman Gilbarco Google With whom: T- Mobile, HTC, Partnership Qualcom, Motorola Alliance When: unknown T-Mobile Google HTC Alliance Qualcomm Motorola Copyright © 2009 TEMIS –All rights reserved 6
  • From Metadata to Knowledge! Copyright © 2009 TEMIS –All rights reserved 7
  • What is Text Mining? v Text Mining is an information access technology… v Text Mining generates Knowledge v Text Mining serves information consumers & producers Text Mining Back-End Data Repository Text Mining Front-End (Text Analytics) Copyright © 2009 TEMIS –All rights reserved 8
  • 1. Enhanced Search ExperienceFrom standard keyword search…. Simple recognition of words… Copyright © 2009 TEMIS –All rights reserved 9
  • 1. Enhanced Search Experience … to Entity & Fact search! •Make comprehensive and precise search End-User •Get more relevant documents Benefits •Find what you don’know! t Copyright © 2009 TEMIS –All rights reserved 10
  • 2. Faceted NavigationFrom “ narrow your search” …. Copyright © 2009 TEMIS –All rights reserved 11
  • 2. Faceted Navigation… to multi-dimensional faceted navigation Point & Click filtering Ability to combine several filters at once (and/or) Self-adjusting filters to refine the search •Get a quick vision of document content End-User •Navigate within context-relevant information Benefits •Rapidly focus on targeted documents Copyright © 2009 TEMIS –All rights reserved 12
  • 3. Data Analysis and ReportingFrom bug view …. Copyright © 2009 TEMIS –All rights reserved 13
  • 3. Data Analysis and Reporting … to bird- eye view! •Visualize key Entities & Facts (pie/bar charts) End-User •Detect Entities & Facts dependencies (matrix charts) Benefits •Zoom in & out by drilling anywhere Copyright © 2009 TEMIS –All rights reserved 14
  • 4. Information DiscoveryFrom flat list of documents …. Copyright © 2009 TEMIS –All rights reserved 15
  • 4. Information Discovery … toinformation network Discovery Search Tools Panel Entities Proofs Facts •Search in knowledge, not in documents End-User •Get a graphical representation of knowledge Benefits •Discover information by navigating within Facts Copyright © 2009 TEMIS –All rights reserved 16
  • Semantic Enrichment at the Core Automatic Entity & Facts Taxonomy Content Categorization Extraction Management Editors Related Topics Editorial Web Content Extraction & Content Management Similarity Management Detection Smart Text Mining Linking Content Enrichment Trends Analysis Product & Charting Visitors & customers Management Sentiment Analysis Content Metadata Annotation Extraction Original Content Journal Scans Expert Interviews Event Reports Copyright © 2009 TEMIS –All rights reserved 17
  • Benefits to Information Producers Increase stickiness of website to maximize ad revenue or subscription utilization! v Create more engaging, longer lasting user visits • Richer user experience with context sensitive information • Enhanced page views per visits • Exposing the “long tail” through suggestions and linking • Integrate more content at a fraction of the cost v Establish your web properties as a community gateway • “70% of all searches do NOT start on Google/MSN/Yahoo” says Sue Feldman at IDC Research • Smart search and navigation are critical to user’ experience s Copyright © 2009 TEMIS –All rights reserved 18
  • Re-Packaging Content – Elsevier v Objective • Develop a revolutionary database indexing the last 28 years in chemistry patent • Provide an exceptional users’experience by using “smart content” v Results • ~20 Million Chemistry Patent documents • Searchable by chemical reactions, solvents, reactants directly extracted from the documents • Released by Elsevier-MDL in Nov. 2004 v Currently • TEMIS distributes the Chemical Entities Relationships Annotator in partnership with Elsevier Copyright © 2009 TEMIS –All rights reserved 19
  • Exposing the Long Tail – Springer v Objective • Mapping of meaningful words and phrases in journal articles to encyclopedia entries • Identification of related documents in a pool of over three million journal articles v Solution • Indexing of incoming journal articles to link journal articles with the related encyclopedia entry • Creation of semantic fingerprint for each journal article to allow search engine calculate degree of relationship • Integration with Springer’ search engine s v Benefits • Increased product sales by improving content linking Copyright © 2009 TEMIS –All rights reserved 20
  • Answering Burning Questions – EFL v Objectives • Extract numerical data from case law to enhance information access for lawyers. v Solution • Luxid® with custom annotators (address, activity, compensation, age, turnover… ) • Export numerical data as metadata to a search engine. v Benefits • Productivity gain to extract and validate metadata • Allowing to treat huge amount of case law Copyright © 2009 TEMIS –All rights reserved 21
  • Questions?Thank you!SSP 2009 Annual MeetingChris Beguel – Director of Sales – TEMISBaltimore, MD – May 09