Open Calais Release 4.0

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    First draft, with beautiful work by Sagit. Note that ALL text is editable.

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    Open Calais Release 4.0 - Presentation Transcript

    1. Calais Thomson Reuters Calais Initiative: Calais 4.0 ~ January, 14, 2009 Thomas (“Tom”) Tague and Krista Thomas
    2. Overview
      • Going to discuss five basic topics
        • What is Calais?
        • Why we’re doing it & what our goals are
        • How it works / What’s under the hood?
        • A few examples
        • Where it’s headed
    3. Calais? What’s Calais? As seen from U.K & the Continent As seen from North America As seen by us
    4. Calais? What’s Calais?
      • A semantic metadata generation service that extracts entities, facts and events from unstructured text
      • Creates linkages from extracted entities to linked data ecosystem
      • Provides a transportation layer for rich semantic metadata from producers to consumers
      • Details to follow….
    5. Why We’re Doing It
      • Two simple answers:
        • Hyper-evolution of capabilities – better, faster, stronger
        • The walled garden content world
    6. Our Goals / The Capabilities We Want to Deploy
      • Let’s state them here and then walk through why we have these goals
        • Derive semantic metadata from textual assets
        • Use that semantic metadata to create entry points into the linked data ecosystem
        • Provide a simple mechanism for the sharing of semantic metadata about textual content assets
    7. 1: Semantics from Text: The Text Problem
      • People consume text
      • Most of it isn’t semantically enabled
      • Most of it won’t be semantically enabled
      • This isn’t about standards – microfromats vs RDFa vs whatever.
      • Why: Latency, cost and short shelf-life
    8. 1: Semantics from Text: The Text Problem
      • Target areas where:
        • The economics don’t support metadata creation
        • The value of metadata is potentially high
        • The value of aggregated metadata is potentially extremely high
      Seconds Years Seconds Years Tweets Blogs News Scient. Pubs Great Novels Latency Shelf Life
    9. 2: Getting from Text to the Linked Data Ecosystem
    10. The Linked Data Cloud
    11. 3: Semantic Metadata Transport Layer
      • I’m a content producer. We’ve loaded the car with rich semantic metadata
        • I’m sharing it within my four walls
        • How do I transport it to my consumers?
        • RSS / Atom, XML, Proprietary data feeds, Content API’s
    12. How it Works – Under the Hood of Calais
    13. How it Works – Under the Hood of Calais Calais Web Service ClearForest NLP Engine Rule Base Lexicons RDF Disambig. Engine Reference Data Assets Metadata Management Document Level Metadata Entity Level Linked Data and … Output Formatting Stat Tools
    14. How You Can Use It – the SemHead version
      • Send unstructured text
        • Get back document categorization, entities, facts and events – with document and entity level URI’s
      • Syndicate Metadata
        • Send unstructured text
        • Share /syndicate the document GUID
      • Access Endpoints
        • Use entity level URI
        • Access entity level Linked Data endpoints & TR Content
    15. Entities, Facts & Events
      • Anniversary, City, Company, Continent, Country, Currency, EmailAddress, EntertainmentAwardEvent, Facility, FaxNumber, Holiday, IndustryTerm, MarketIndex, MedicalCondition, MedicalTreatment, Movie, MusicAlbum, MusicGroup, NaturalDisaster, NaturalFeature, OperatingSystem, Organization, Person, PhoneNumber, Product, ProgrammingLanguage, ProvinceOrState, PublishedMedium, RadioProgram, RadioStation, Region, SportsEvent, SportsGame, SportsLeague, Technology, TVShow, TVStation, URL
      • Acquisition, Alliance, AnalystEarningsEstimate, AnalystRecommendation, Bankruptcy, BonusShares, BusinessRelation, Buybacks, CompanyAffiliates, CompanyCustomer, CompanyEarningsAnnouncement, CompanyEarningsGuidance, CompanyInvestment, CompanyLegalIssues, CompanyLocation, CompanyMeeting, CompanyReorganization, CompanyTechnology, CompanyTicker, ConferenceCall, CreditRating, EmploymentRelation, FamilyRelation, FDAPhase, IPO, JointVenture, ManagementChange, Merger, MovieRelease, MusicAlbumRelease, PatentFiling, PatentIssuance, PersonAttributes, PersonCommunication, PersonEducation, PersonEmailAddress, PersonPolitical, PersonPoliticalPast, PersonProfessional, PersonProfessionalPast, PersonRelation, PersonTravel, Quotation, SecondaryIssuance, StockSplit
    16. Extending Calais’ Reach
      • More than just a web service – a growing collection of tools and applications to make it valuable in the real world
      Calais Browser Extensions Gnosis Content Management Tools WordPress Drupal UIMA Development Tools & Libraries PHP Ruby JAVA .NET Applications And more… TopBraid RSS Tagger Powerhouse LinkedFacts Wirecatch FeedShaver
    17. Calais progress to date
      • Launched in late January, 2008
      • 9,000 developers have joined OpenCalais.com
      • Approx. 1 million content ‘transactions’ per day
      • Delivered four major update releases
      • Lots of interesting apps
        • The Mail & Guardian Online ( http:// www.mg.co.za / )
        • www.powerhousemuseum.com
        • Gist.whistlehog.com
        • http://www.semanticproxy.com
    18. Example: The Mail & Guardian Online, South African Newspaper
      • Using Calais to metatag new and historical articles, and:
        • Build an index or topics A-Z
        • Pull out automatic related articles or pictures
        • Create news alerts on companies or people
        • Pull up maps for the countries named in articles
        • Predict readers’ interests based on browsing habits
        • Create tag clouds, showing popular subjects, people, etc.
      Using Calais to optimize search and navigation; drive consumer engagement
    19. Example: Gist - today’s news filtered by people, places & events GIST uses Calais to prioritize stories, rank newsmakers & reveal trends / reader demand. It automatically aggregates multiple news sources and slots them into topic.
    20. Example: The Powerhouse Museum in Sydney Using Calais to tag historical archives & using tags as search terms
    21. Example: IT Healthcare News Using Calais to surface ambient “related content”
    22. Examples
      • Those are examples of first generation uses. Some of what we’re seeing in the pipeline:
        • Social Resume analysis
        • Investigative Journalism*
        • Museum metadata coalitions
    23. Investigative Journalism FOIA Contract Documents Calais Web Service Company:Person FamilyRelation News Calais Web Service Company:Contract Company:Affiliation Big Fuzzy Graph
    24. What’s new in Release 4?
      • Release 4 – What’s New?
        • Linked data for approximately 25 entities
        • A start at Thomson Reuters contributed content
        • Metadata hosting and transport
        • Basic French
        • Published RDFS Ontology
        • New entities / relationships
          • Products
          • Competitive intelligence
          • Expanded document level categorization
    25. What’s in the Pipeline?
      • 2009 (this is a fuzzy list)
        • Person disambiguation @ domain level?
        • Other disambiguation
        • Dramatic expansion of endpoints (entities & events)
        • Calais as hub
        • Exposure of the IDE?
        • User managed lexicons
        • Languages
        • Opt-in SPARQL Endpoint?
      • www.opencalais.com
        • Gallery – code and applications examples
        • Forums
        • Documentation

    + Krista ThomasKrista Thomas, 10 months ago

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