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

Using the BLS Template DO NOT DELETE (slide hidden from show) Two CORE slides – Presentation Title & Contact Info – are mandatory All CONTENT slides are optional – Do not mix & match slides from BLS Blue CONTENT Slides and BLS White CONTENT Slides – 2 slides are not editable: BLS Mission & BLS Vision Using the slides – To insert slide: Home tab > use New Slide drop down to select – To change current layout: Home tab > use Layout drop down to select – To change background color for all CONTENT slides: Design tab > (while on a CONTENT slide) scroll mouse over to either BLS Blue Content Slides design or BLS White CONTENT Slides design Footers (e.g. date or page numbers) – Insert tab > Header & Footer button > Slide tab > check/uncheck appropriate boxes Font types and sizes are preset – Font will shrink as text expands but, for readability, use font size 20+ Sample charts provided are also hidden from show Save file as ppt file NOT pptx (offsite software may not recognize pptx)

The BLS Mission The Bureau of Labor Statistics (BLS) is the principal fact-finding agency for the Federal Government in the broad field of labor economics and statistics. The BLS collects, processes, analyzes, and disseminates essential statistical data to the public, Congress, Federal agencies, State and local governments, business, and labor.

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 Berners-Lee  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 METIS5

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 METIS6

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 METIS7

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 METIS8

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 METIS9

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? METIS10

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 METIS11

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 METIS12

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 METIS13

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 METIS14

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 METIS15

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?) METIS16

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 METIS17

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 METIS18

Semantic Web Technologies Ontologies  Logics – Predicate calculus – Description logic – First order logic – Others  Low to high formality METIS19

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 METIS20

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 METIS21

Semantic Web Technologies Typical languages – cont’d  RDF and OWL – W3C specifications  Common Logic – ISO/IEC – 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 METIS22

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 METIS23

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 METIS24

Semantic Web and Metadata Management Consistent & Compete ≠> Useful Discovery doesn’t need new methods Registries are designed for this  SDMX  ISO/IEC  Library card catalog METIS25

Semantic Web and Metadata Management Judgment  SW offers no help Meaning  Metadata management already solves  METIS members are experts METIS26

Conclusion Verdict  SW not offering much new SW descriptions  Make hard problems seem easy  Make easy problems seem hard – Often the “sexy” stuff METIS27

Contact Information Daniel Gillman