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Open Issues on Semantic Web. Daniel W. Gillman US Bureau of Labor Statistics. Outline. Semantic Web – Description Scenario Problems Semantic Web Technologies Semantic Web and Metadata Management Analysis Identify problems / use scenario Discovery, Judgment, Meaning

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open issues on semantic web

Open Issues on Semantic Web

Daniel W. Gillman

US Bureau of Labor Statistics

outline
Outline
  • Semantic Web – Description
  • Scenario
  • Problems
  • Semantic Web Technologies
  • Semantic Web and Metadata Management
  • Analysis
    • Identify problems / use scenario
    • Discovery, Judgment, Meaning
  • Not Semantic Web criticism / Stimulus for debate

METIS

semantic web description
Semantic Web - Description
  • Berners-Lee -- 1999
    • I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize.

METIS

semantic web description1
Semantic Web - Description
  • Web pages, readable
    • B y computer
  • Instead, now, humans
    • Determine height of Mt Everest
    • Reserve table at favorite restaurant
    • Find best prices for tires for the car
  • Semantic Web will demand more

METIS

semantic web description2
Semantic Web - Description
  • Two new IT artifacts
    • Web Services
    • Ontologies
  • Service
    • Set of events with a defined interface
  • Web Service
    • Software designed to support interoperable machine-to-machine interaction over a network

METIS

semantic web description3
Semantic Web - Description
  • Ontology
    • Set of concepts, the relations among them, and a computational description
    • Purpose is to be able to reason, i.e., make inferences
  • Knowledge representation languages
    • Bridge between web service and ontology

METIS

scenario
Scenario
  • “America’s Safest Cities”
    • by Zack O’Malley Greenburg
    • 26 October 2009
    • Forbes Magazine
  • Rank cities by “livability”
    • Workplace fatalities
    • Traffic fatalities
    • Violent crimes
    • Natural disaster risk

METIS

scenario1
Scenario
  • Base comparison on MSA
    • Metropolitan statistical area
  • Rank MSAs based on
    • Numerical ranking for each measure
    • Sum of rankings
  • Questions
    • Can we find such data?
    • If so, where?

METIS

scenario2
Scenario
  • Finding data -- Discovery
    • Workplace fatalities
      • Bureau of Labor Statistics
      • Data based on MSA
      • Data given as number, not rate
    • Traffic fatalities
      • National Highway Traffic Safety Administration
      • Data based on city, not MSA
      • Based on rates

METIS

scenario3
Scenario
  • Violent crime
    • Federal Bureau of Investigation
    • Based on MSA
    • Given as rate
  • Natural disaster risk
    • SustainLane.Com
    • Not federal site, based on government data
    • Data based on city, but only a few
    • No data, no rates, just a rank

METIS

scenario4
Scenario
    • Using data – Judgment
    • Unit of analysis = MSA
  • Questions
    • How can we combine this data?
    • Can we harmonize the differences?
    • City as proxy for MSA?
  • Decisions are
    • Qualitative
    • Require human judgment

METIS

scenario5
Scenario
  • How do we know
    • MSA vs. city
    • Number vs. rate
    • Rank vs. rate?
  • Understanding – Meaning
  • Requires
    • Links from data sets to metadata
    • Good metadata model for data semantics
    • METIS is good at this

METIS

problems
Problems
  • Meaning
    • Easy – needs agency metadata
    • Link meanings to data
      • Straightforward
      • Mechanical, once metadata is captured
  • Discovery
    • Harder –
      • Difficult search
      • Takes a lot of work
      • Numerous comparisons
      • Not easy to know when to stop

METIS

problems1
Problems
  • Judgment
    • Very hard –
      • Difficult to see how to automate
      • Case by case basis
  • If proxy OK?
    • Need population for MSA
    • Again, where?
      • Discovery (Census Bureau)
      • Judgment (Appropriate?)
      • Meaning (Data elements correct?)

METIS

semantic web technologies
Semantic Web Technologies
  • Web services
    • Any action in Semantic Web
    • Several kinds
    • Operation required? Web service called
  • Examples based on scenario
    • Read data from a data set
    • Display data dictionary of data set
    • Calculate rates, ranks, and overall rank

METIS

semantic web technologies1
Semantic Web Technologies
  • Ontologies
    • Concept systems
      • Set of concepts
      • Relations among them
    • Computational description
      • How one makes inferences
      • Logical system
    • Means for organizing knowledge
      • Concepts organized for some purpose

METIS

semantic web technologies2
Semantic Web Technologies
  • Ontologies
    • Logics
      • Predicate calculus
      • Description logic
      • First order logic
      • Others
    • Low to high forma lity

METIS

semantic web technologies3
Semantic Web Technologies
  • Knowledge representation languages
    • Bridge between ontology and web service
    • Service uses KRL to make inferences
  • Typical languages
    • RDF – Resource Description Framework
      • Based on “triples”
        • Subject – verb – object
      • Triples can be linked
        • Object of one is subject of another
      • Creates Directed Graph structure

METIS

semantic web technologies4
Semantic Web Technologies
  • Typical languages – cont’d
    • OWL – Web Ontology Language
      • Comes in 3 main types
        • OWL – lite
          • More powerful than RDF, easiest, a DL
        • OWL – DL
          • More powerful than OWL – lite, a DL also
        • OWL – full
          • Equivalent to RDF-Schema, almost FOL
          • Most powerful OWL, hard to implement

METIS

semantic web technologies5
Semantic Web Technologies
  • Typical languages – cont’d
    • RDF and OWL – W3C specifications
    • Common Logic – ISO/IEC 24707
      • Very powerful
      • Full FOL, including some extensions
  • However – Using KR ≠> Ontology
  • KR languages – Difficult to implement
      • Work to build non-trivial ontology is huge
        • Subject matter experts
        • Terminology experts
        • KR and logic experts

METIS

semantic web and metadata management
Semantic Web and Metadata Management
  • Metadata play central role in SW
  • Linked Data – newer aspect of SW
    • Berners-Lee given credit again
    • Laid out 4 criteria
      • Use URIs to identify things.
      • Use HTTP URIs for dereferencing
      • Provide useful metadata when URI dereferenced.
      • Include links to other, related URIs

METIS

semantic web and metadata management1
Semantic Web and Metadata Management
  • 2 main reactions:
    • 1) No difference with traditional metadata management
    • 2) Begs the question
      • How does one FIND the right URI (URL)?
  • Answer – Ontologies! – See above!
  • Successful ontology
    • Consistent
    • Complete
    • Useful

METIS

semantic web and metadata management2
Semantic Web and Metadata Management
  • Consistent & Compete ≠> Useful
  • Discovery doesn’t need new methods
  • Registries are designed for this
    • SDMX
    • ISO/IEC 11179
    • Library card catalog

METIS

semantic web and metadata management3
Semantic Web and Metadata Management
  • Judgment
    • SW offers no help
  • Meaning
    • Metadata management already solves
    • METIS members are experts

METIS

conclusion
Conclusion
  • Verdict
    • SW not offering much new
  • SW descriptions
    • Make hard problems seem easy
    • Make easy problems seem hard
      • Often the “sexy” stuff

METIS