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CSHALS 2010 W3C Semanic Web Tutorial


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These slides were presented as part of a W3C tutorial at the CSHALS 2010 conference ( The slides are adapted from a longer introduction to the Semantic Web available at …

These slides were presented as part of a W3C tutorial at the CSHALS 2010 conference ( The slides are adapted from a longer introduction to the Semantic Web available at .

A PDF version of the slides is available at .

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  • One of the goals of this tutorial is to de-mystify the all of the names of technologies, tools, projects, etc. that swirl around the Semantic Web story.And since I saw that as I researched this presentation, everyone seems to like this particular Gary Larson cartoon, it behooved me to include it.
  • The good – emphasize the importance of the foundational layers (URIs and RDF) ; emphasizes the long-term roadmap/vision of what’s needed for the Semantic WebThe bad – implies that perhaps things can’t be taken serious until all the pieces are in place ; implies an order to the research ; various versions of the cake tell different stories (importance of XML, absence of query, lack of UI/application layer, …)Valentin Zacharias wrote about the “infamy” part of the layer cake here:
  • The Ontology/ontology dichotomy is captured well by Jim Hendler at
  • Definition.
  • Prescriptive.
  • Descriptive.
  • Formal.
  • The first is as opposed to relational tables or XML schemas where the schema needs to be explicitly adjusted to accommodate whatever data is being merged.The second is due to the expressivity of the model – can handle lists, trees, n-ary relations, etc.The third is as opposed to table & column identifiers or XML attribute names.
  • Definition.
  • Prescriptive.
  • Descriptive.
  • Descriptive (part 2). This is leagues ahead of the situation with SQL!
  • Definition.
  • Definition.
  • Definition.
  • Transcript

    • 1. The Semantic Web LandscapeA Practical Introduction
      Lee Feigenbaum
      VP Technology & Standards, Cambridge Semantics
      Co-chair, W3C SPARQL Working Group
      For CSHALS 2010 Tutorial Attendees
      February 24, 2010
    • 2. The W3C HCLS interest group set out to use Semantic Web technologies to receive precise answers to a complex question:
      A Motivating Example: Drug Discovery
      Find me genes involved in signal transduction that are related to pyramidal neurons.
    • 3. General search
      223,000 hits, 0 results
    • 4. Domain-limited search
      2,580 potential results
    • 5. Specific databases
      Too many silos!
    • 6. A Semantic Web Approach
      Integrate disparate databases…
      Entrez Gene
      Gene Ontology

    • 7. A Semantic Web Approach (cont’d)
      …so that onequery…
    • 8. A Semantic Web Approach (cont’d)
      …(trivially) spans several databases…
    • 9. A Semantic Web Approach (cont’d)
      …to deliver targeted results…
    • 10. Agreement on common terms and relationships
      Incremental, flexible data structure
      Good-enough modeling
      Query interface tailored to the data model
      What’s the trick?
    • 11. What is the semantic web?
    • 12. Names
    • 13. Semantic Web
      Web of Data
      Giant Global Graph
      Data Web
      Web 3.0
      Linked Data Web
      Semantic Data Web
    • 14. “The Semantic Web” a.k.a “Linked Open Data”
      Augments the World Wide Web
      Represents the Web’s information in a machine-readable fashion
      …targeted search
      …data browsing
      …automated agents
      What is it & why do we care? (1)
      World Wide Web : Web pages :: The Semantic Web : Data
    • 15. “Semantic Web technologies”
      A family of technology standards that ‘play nice together’, including:
      Flexible data model
      Expressive ontology language
      Distributed query language
      Drive Web sites, enterprise applications
      What is it & why do we care? (2)
      The technologies enable us to build applications and solutions that were not possible, practical, or feasible traditionally.
    • 16. A common set of technologies:
      ...enables diverse uses
      ...encourages interoperability
      A coherent set of technologies:
      …encourage incremental application
      …provide a substantial base for innovation
      A standard set of technologies:
      ...reduces proprietary vendor lock-in
      ...encourages many choices for tool sets
      A Common & Coherent Set of Technology Standards
    • 17. The (In)Famous Layer Cake
    • 18. Semantic Web Technology Timeline
    • 19. As technologies & tools have evolved, Semantic Web advocates have progressed through stages:
      2010: Where we are
    • 20. 2010: Where we’re not
      Image from Trey Ideker via Enoch Huang
      Semantic Web technologies are not a ‘magic crank’ for discovering new drugs (or solving other problems, for that matter)!
    • 21. 2010: Where we’re not (cont’d)
      XML vs. RDF?
      “Ontology” vs. “ontology”?
      Data integration vs. reasoning vs. KBs vs. search vs. app. development vs. …
      Semantic Web vs. Linked Data?
      The Semantic Web still suffers from confusing and conflicting messaging, each of which asserts it’s “correct”.
    • 22. 2010: Where we’re not (cont’d)
      People with appropriate skill sets for designing & building Semantic Web solutions are not widely available.
    • 23. 2010: Where we’re not (cont’d)
      We don’t yet have standard solutions for privacy, trust, probability, and other elements of the Semantic Web vision.
    • 24. What do Semantic Web solutions look like?
    • 25. RDF is…
      Resource Description Framework
    • 26. RDF is…
      The data model of the Semantic Web.
    • 27. RDF is…
      A schema-less data model that features unambiguous identifiers and named relations between pairs of resources.
    • 28. RDF is…
      A labeled, directed graph of relations between resources and literal values.
      RDF graphs are collections of triples
      Triples are made up of a subject, a predicate, and an object
      Resources and relationships are named with URIs
    • 29. “Lee Feigenbaum works for Cambridge Semantics”
      “Lee Feigenbaum was born in 1978”
      “Cambridge Semantics is headquartered in Massachusetts”
      Example RDF triples
      works for
      born in
      Lee Feigenbaum
      Cambridge Semantics
      Lee Feigenbaum
      Cambridge Semantics
    • 30. Triples connect to form graphs
      lives in
      born in
      works for
      Lee Feigenbaum
      Cambridge Semantics
    • 31. The graph data structure makes merging datawith shared identifiers trivial
      Triples act as a least common denominatorfor expressing data
      URIs for naming remove ambiguity
      …the same identifier means the same thing
      Why RDF? What’s different here?
    • 32. Why RDF? Incremental Integration
    • 33. RDF is the model, for which there are several concrete syntaxes:
      RDF/XML – standard, complex XML syntax
      Turtle – common, textual, triples-oriented syntax
      N3 – more expressive superset of Turtle
      N-Triples – textual, line-oriented, useful for streaming
      What does RDF look like?
      When writing RDF by hand and in many guides, examples, and discussions these days, you’ll see Turtle most often.
    • 34. Write a triple by writing its parts separated by spaces (subject predicate object)
      A Bit of Turtle
      @prefix ex: <> .
      @prefix geo: <http://geonames.example/> .
      ex:LeeFeigenbaumex:employerex:CambridgeSemantics .
      ex:LeeFeigenbaumex:birthYear 1978 .
      ex:CambridgeSemanticsex:headquartersgeo:BostonMA .
      geo:BostonMAex:population 574000 .
    • 35. SPARQL is…
      SPARQL Protocol And RDF Query Language
    • 36. SPARQL is…
      The query language of the Semantic Web.
    • 37. SPARQL is…
      A SQL-like language for querying sets of RDF graphs.
    • 38. SPARQL is…
      A simple protocol for issuing queries and receiving results over HTTP. So…
      Every SPARQL client works with every SPARQL server!
    • 39. SPARQL lets us:
      Pull information from structured and semi-structured data.
      Explore data by discovering unknown relationships.
      Query and search an integrated view of disparate data sources.
      Glue separate software applications together by transforming data from one vocabulary to another.
      Why SPARQL?
    • 40. Dealer 1
      Dealer 2
      Dealer 3
      ERP / Budget
      EPA Fuel Efficiency
      SPARQL Query Engine
      What automobiles get more than 25 miles per gallon, fit within my department’s budget, and can be purchased at a dealer located within 10 miles of one of my employees?
      SELECT ?automobile
      WHERE { ?automobile a ex:Car ; epa:mpg ?mpg ;
      ex:dealer ?dealer .
      ?employee a ex:Employee ; geo:loc ?loc .
      ?dealer geo:loc ?dealerloc .
      FILTER(?mpg > 25 &&
      geo:dist(?loc, ?dealerloc) <= 10) .
      Web dashboard
      SPARQL query
    • 41. – querying life sciences data
    • 42. – querying life sciences data
    • 43. 3 pieces of the Semantic Web technology stack are about describing a domain well enough to capture (some of) the meaning of resources and relationships in the domain
      RDF Schema
      From the explicit to the inferred
      Apply knowledge to data to get more data.
    • 44. RDFS is…
      RDF Schema
    • 45. Elements of:
      Vocabulary (defining terms)
      I define a relationship called “prescribed dose.”
      Schema (defining types)
      “prescribed dose” relates “treatments” to “dosages”
      (my prescribed dose is 2mg; therefore 2mg is a dosage)
      Taxonomy (defining hierarchies)
      Any “doctor” is a “medical professional”
      (therefore Dr. Brown is a medical professional)
      RDF Schema is…
    • 46. WOL OWL is…
      Web Ontology Language
    • 47. Elements of ontology
      Same/different identity
      “author” and “auteur” are the same relation
      two resources with the same “ISBN” are the same “book”
      More expressive type definitions
      A “cycle” is a “vehicle” with at least one “wheel”
      A “bicycle” is a “cycle” with exactly two “wheels”
      More expressive relation definitions
      “sibling” is a symmetric predicate
      the value of the “favorite dwarf” relation must be one of “happy”, “sleepy”, “sneezy”, “grumpy”, “dopey”, “bashful”, “doc”
      OWL is…
    • 48. A class is a (named) collection of things with similar attributes
      OWL: Rich Class Definitions
    • 49. A class is a (named) collection of things with similar attributes
      OWL: Rich Class Definitions
    • 50. A class is a (named) collection of things with similar attributes
      OWL: Rich Class Definitions
    • 51. OWL: Rich Class Definitions
    • 52. RIF is…
      Rules Interchange Format
    • 53. Standard representation for exchanging sets of logical and business rules
      Logical rules
      A buyer buys an item from a seller if the seller sells the item to the buyer
      A customer becomes a "Gold" customer as soon as his cumulative purchases during the current year top $5000
      Production rules
      Customers that become "Gold" customers must be notified immediately, and a golden customer card will be printed and sent to them within one week
      For shopping carts worth more than $1000, "Gold" customers receive an additional discount of 10% of the total amount
      RIF is…
    • 54. Fantasy Land Architecture
      Ontology / Schema
      Custom UI
      Custom UI
      Custom UI
      Custom UI
      Custom UI
      Custom UI
    • 55. Reality
      LDAP Directory
      Custom UI
      Custom UI
      Custom UI
      Custom UI
      Custom UI
      Custom UI
    • 56. GRDDL is…
      Gleaning Resource Descriptions from Dialects of Language
    • 57. GRDDL is…
      A method for authoritatively getting RDF data from XML and XHTML documents.
    • 58. GRDDL is…
      A mechanism for authoritatively deriving RDF data from families of XML and XHTML documents.
    • 59. RDB2RDF is…
      Relational Database toRDF
    • 60. RDB2RDF is…
      A W3C Working Group to define a standard way to map from relational databases to RDF (and SPARQL).
    • 61. A simple set of 4 guidelines for publishing RDF data on the Web (over HTTP)
      Developed by Tim Berners-Lee in 2006
      Use URIs as names for things
      • Globally unique identity
      Use HTTP URIs
      • Everyone has a Web browser/client
      When someone looks up a URI, provide useful information
      • …in the form of RDF data
      Include links to other URIs
      • Foster discovery of additional information
      Linked Data is…
    • 62. The LOD “cloud”, March 2009
    • 63. Application specific portions of the cloud
      • Notably, bio-related data sets (in light purple)
      • 64. some by the W3C “Linking Open Drug Data” task force
    • RDFa is…
      RDF in Attributes
    • 65. RDFa is…
      A collection of HTML attributes that allow RDF to be embedded directly in Web pages.
    • 66. Don’t Repeat Yourself (DRY)
      In-context metadata (copy & paste)
      Authoritative (no screen scrapig)
      Why RDFa?
    • 67. RDFa in action
    • 68. Semantic Web landscape today
    • 69. Semantic Web Tools
      In 2010, there are a wide variety of open-source and commercial Semantic Web tools available.
    • 70. Triple stores
      Built on relational database
      Native RDF store
      Development libraries
      Full-featured application servers
      Types of RDF Tools
      Most RDF tools contain some elements of each of these.
    • 71. Community-maintained lists
      Emphasis on large triple stores
      Michael Bergman’s Sweet Tools searchable list:
      Finding RDF Tools
    • 72. Query engines
      Things that can run queries
      Most RDF stores provide a SPARQL engine
      Query rewriters
      E.g. to query relational databases (more later)
      Things that accept queries on the Web and return results
      Client libraries
      Things that make it easy to ask queries
      Types of SPARQL Tools
    • 73. Community-maintained list of query engines
      Publicly accessible SPARQL endpoints
      Michael Bergman’s Sweet Tools searchable list:
      Finding SPARQL Tools
    • 74. Editors/environments
      Oiled, Protégé, Swoop, TopBraid, Ontotrack, …
      Developing Tools and Infrastructure
    • 75. Editors/environments
      Oiled, Protégé, Swoop, TopBraid, Ontotrack, …
      Reasoning systems
      Cerebra, FaCT++, Kaon2, Pellet, Racer, CEL, …
      Developing Tools and Infrastructure
    • 76. Visualizing and Publishing Vocabularies
    • 77. Reusable, public ontologies
      The Event Ontology
      Measurement Units Ontology
    • 78. Community-maintained list:
      GRDDL tools
      Most GRDDL tools are adapters to existing RDF stores or SPARQL engines to allow loading or querying data from XML and XHTML sources.
    • 79. What about… everything else?
      Standards don’t yet exist, but many tools exist to derive RDF and/or run SPARQL queries against other sources of data.
    • 80. LDAP Directories
      Squirrel RDF
    • 81. Excel spreadsheets
      Anzo for Excel
    • 82. Web-based data sources
      Virtuoso Sponger Cartridges
    • 83. Unstructured Text
    • 84. Unstructured Text
      Zemanta Web Service
    • 85. On the Web
      Google, Yahoo!
      Best Buy
      NY Times
      US Government
      UK Government
      Where is it being used?
    • 86. Industries
      Oil & Gas (integration, classification)
      Finance (structured data, ontologies, XBRL)
      Publishing (metadata)
      Government (structured data, metadata, classification)
      Libraries & museums (metadata, classification)
      IT (rapid application development & evolution)
      Where is it being used?
    • 87. Health Care
      Cleveland Clinic
      Clinical research
      Data integration, classification (= better search)
      UT School of Health
      Public health surveillance
      SAPPHIRE—classification, ontology-driven development
      Clinical Decision Support
      Agile, rule-driven, scalable in the face of change
      Where is it being used?
    • 88. Life Sciences
      Agile knowledgebases at Pfizer
      Target assessment at Eli Lilly
      Integrated information links at Novartis
      Astra Zeneca, J&J, UCB, …
      Where is it being used?
      CSHALS chronicles many of these uses and many more.
    • 89. Take-away Advice
    • 90. These are horizontal, enabling technologies.
      But they apply particularly well to problems with these characteristics:
      Heterogeneous data from multiple sources
      Increasing reliance on connections within this data
      Rapidly changing information needs
      Significant early-mover advantage
      Large amounts of data that would benefit from classification
      Why are Semantic Web technologies appropriate for the life sciences?
      Many tactical and strategic challenges in the life sciences industry feature these traits.
    • 91. Getting Started with Semantic Web technologies
      Don’t boil the ocean.
    • 92. Getting Started with Semantic Web technologies
      Goal: quick tactical wins on the path to large strategic value
      Be sure to consider the operational ramifications
      Who does what differently?
      Ideal Semantic Web projects/applications have an incremental path towards broad deployment that generates demonstrable value along the way
    • 93. Look beyond the core Semantic Web capabilities and consider:
      integration with existing enterprise systems
      development & extension models
      deployment, logging, maintenance, backup
      user experience
      Choose practical, enterprise-ready tools
      If you choose to build new components and assemble existing components together, it’s quite likely you’ll end up reinventing the wheel.
    • 94. What level of expertise is necessary?
      Technologies only?
      Technologies + API?
      Technologies + tooling?
      Tooling only?

      How will we acquire the expertise?
      In-house (and if so, how?)
      Vendor services
      3rd-party services
      Open-source community
      Plan for Acquiring Expertise
    • 95. I’m always happy to field questions & engage in discussion:
      Thanks & Discussion