Open issues on semantic web
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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 formality

    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


    Daniel gillman gillman daniel@bls gov

    Daniel [email protected]


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