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Open Issues on Semantic Web

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

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  1. Open Issues on Semantic Web Daniel W. Gillman US Bureau of Labor Statistics

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. Semantic Web Technologies • Ontologies • Logics • Predicate calculus • Description logic • First order logic • Others • Low to high forma lity METIS

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. Semantic Web and Metadata Management • Judgment • SW offers no help • Meaning • Metadata management already solves • METIS members are experts METIS

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

  26. Daniel Gillmangillman.daniel@bls.gov

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