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

Open Issues on Semantic Web
Daniel W. Gillman
US Bureau of Labor Statistics
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
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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.
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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
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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
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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
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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
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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?
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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
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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
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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
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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
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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
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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?)
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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
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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
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Semantic Web Technologies

Ontologies
Logics
– Predicate calculus
– Description logic
– First order logic
– Others
Low to high formality
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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
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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
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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
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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
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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
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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
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Semantic Web and
Metadata Management

Judgment
SW offers no help

Meaning
Metadata management already solves
METIS members are experts
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Conclusion

Verdict
SW not offering much new

SW descriptions
Make hard problems seem easy
Make easy problems seem hard
– Often the “sexy” stuff
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Contact Information
Daniel Gillman
[email protected]