Open Issues on Semantic Web

sounderslipInternet and Web Development

Oct 22, 2013 (4 years and 18 days ago)

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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,
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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

2010
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03
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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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03
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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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03
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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 forma

lity

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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

gillman.daniel@bls.gov