Semantic Web Applications
Semantic Web Adoption
Ivan Herman, W3C
First China Semantic Web Symposium (CSWS 2007), Beijing, China,
2007-11-19
This is just a generic slide set. Should be adapted, reviewed,
possibly with slides removed, for a specific event. Rule of thumb:
on the average, a slide is a minute
Semantic Web adoption
SW technologies go back to few years now
we have a really stable set of specifications since 2004
Lots of tools, software, system, etc, are at our disposal
now
Applications come to the fore
So where are we exactly with adoption?
A way of looking at it
Semantic Technologies
The 2007 Gartner predictions
By 2017, we expect the vision of the Semantic Web []to coalesce
[] and the majority of Web pages aredecorated with some form of
semantic hypertext.
By 2012, 80% of public Web sites will use some level of semantic
hypertext to create SW documents [] 15% of public Web sites will
use more extensive Semantic Web-based ontologies to create semantic
databases
(note: semantic hypertext refers to, eg, RDFa, microformats with
possible GRDDL, etc.)
Source: Finding and Exploiting Value in Semantic Web
Technologies on the Web, Gartner Research Report, May 2007
It often starts with small communities
The needs of a deployment application area:
have serious problem or opportunity
have the intellectual interest to pick up new things
have motivation to fix the problem
its data connects to other application areas
have an influence as a showcase for others
The high energy physics community played this role for the Web
in the 90s
Some SW deployment communities
Some examples: digital libraries, defense, eGovernment, energy
sector, financial services, health care, oil and gas industry, life
sciences
Health care and life science sector is now very active
also at W3C, in the form of an Interest Group
The corporate landscape is moving
Major companies offer (or will offer) Semantic Web tools or
systems using Semantic Web: Adobe, BBN, Oracle, IBM, HP, Software
AG, GE, Northrop Gruman, Altova, Vodafone, Dow Jones,
Others are using it (or consider using it) as part of their own
operations: Novartis, Pfizer, Telefnica, Vodafone, Elsevier,
Some of the names of active participants in W3C SW related
groups: ILOG, HP, Agfa, SRI International, Fair Isaac Corp.,
Oracle, Boeing, IBM, Chevron, Nokia, Merck, Pfizer, T-Mobile,
AstraZeneca, Sun, Eli Lilly,
SWEO enterprise survey
W3Cs SW Education and Outreach Interest Group (SWEO) conducted a
survey in January 2007
Around 50 responses from 10 countries
One of the main results: majority of responders considered
missing skills to be the biggest obstacle
universities have a major role to play
SWEOs use case collection
SWEO is actively collecting SW use cases and case studies
use case: prototype applications within the enterprise
case study: deployed applications, either in an enterprise,
community, governmental, etc sites
SWEOs use case collection
At present there are
16 case studies and 9 use cases (early November 2007)
from 11 different countries around the globe
submitters activity areas include: automotive, broadcasting,
financial institution, health care, oil & gas industry,
pharmaceutical, public and governmental institutions, publishing,
telecommunications,
usage areas include: data integration, portals with improved
local search, business organization, B2B integration,
Remember this URI:
http://www.w3.org/2001/sw/sweo/public/UseCases/
So how do applications look like?
Applications are not always very complex
Eg: simple semantic annotations of data provides easy
integration (eg, with MusicBrainz, Wikipedia, geographic data sets,
etc)
What is needed: some simple vocabularies, simple annotation
annotation an be generated by a server automatically, or
added by the user via some user interface
Semantic hypertext, to use Gartners terminology
Problem: data on a patient goes from one physician to the other
and if the data are not smoothly forwarded then tests are repeated
needlessly. The system does not use any complicated system, just
the basics of RDF usage
Applications are not always very complex
Problem: data on a patient goes from one physician to the other
and if the data are not smoothly forwarded then tests are repeated
needlessly. The system does not use any complicated system, just
the basics of RDF usage
Major application paradigm: data integration
Very important for large application areas (life sciences,
energy sector, eGovernment, financial institutions), as well as
everyday applications (eg, reconciliation of calendar data)
Is the most representative usage both in the SWEO survey as well
as in the use cases and cases studies
Developments are under way at various companies,
institutions
The HCLS Demo
The W3C Health Care and Life Sciences Interest Group (HCLS) has
developed few demonstrations on SW usage
Goal is to show:
the HCLS community how Semantic Web can be used
the SW community how this technology can be useful in this
application are
Prevailing paradigm is data integration
HCLS demo: looking for Alzheimers targets
Signal transduction pathways are considered to be rich in
proteins that might respond to chemical therapy
CA1 Pyramidal Neurons are known to be particularly damaged in
Alzheimers disease
Can we find candidate genes known to be involved in signal
transduction and active in Pyramidal Neurons?
To answer: integrate datasets
W3C HCLS IG has already integrated a number of public datasets
and ontologies
assign URI-s to bio entities
data converted or made reachable in RDF
use reasoners to infer extra triples to increase
expressiveness
query the data with SPARQL and visualization tools
around 400M triples so far
Use SPARQL to integrate
prefix go:
prefix rdfs:
prefix owl:
prefix mesh:
prefix sc:
prefix ro:
select ?genename ?processname
where
{ graph
{ ?paper ?p mesh:D017966 .
?article sc:identified_by_pmid ?paper.
?gene sc:describes_gene_or_gene_product_mentioned_by
?article.
}
graph
{ ?protein rdfs:subClassOf ?res.
?res owl:onProperty ro:has_function.
?res owl:someValuesFrom ?res2.
?res2 owl:onProperty ro:realized_as.
?res2 owl:someValuesFrom ?process.
graph
{{?process go:GO_0007166}
union
{?process rdfs:subClassOf go:GO_0007166 }}
?protein rdfs:subClassOf ?parent.
?parent owl:equivalentClass ?res3.
?res3 owl:hasValue ?gene.
}
graph
{ ?gene rdfs:label ?genename }
graph
{ ?process rdfs:label ?processname}
}
Mesh: Pyramidal NeuronsPubmed: Journal ArticlesEntrez Gene:
GenesGO: Signal Transduction
Inference requiredCourtesy of Susie Stephens, Eli Lilly, Alan
Ruttenberg, Science Commons, and the W3C HCLS IG
Yielding results to the query
DRD1, 1812adenylate cyclase activation
ADRB2, 154adenylate cyclase activation
ADRB2, 154arrestin mediated desensitization of G-protein coupled
receptor protein signaling pathway
DRD1IP, 50632dopamine receptor signaling pathway
DRD1, 1812dopamine receptor, adenylate cyclase activating
pathway
DRD2, 1813dopamine receptor, adenylate cyclase inhibiting
pathway
GRM7, 2917G-protein coupled receptor protein signaling
pathway
GNG3, 2785G-protein coupled receptor protein signaling
pathway
GNG12, 55970G-protein coupled receptor protein signaling
pathway
DRD2, 1813G-protein coupled receptor protein signaling
pathway
ADRB2, 154G-protein coupled receptor protein signaling
pathway
CALM3, 808G-protein coupled receptor protein signaling
pathway
HTR2A, 3356G-protein coupled receptor protein signaling
pathway
DRD1, 1812G-protein signaling, coupled to cyclic nucleotide
second messenger
SSTR5, 6755G-protein signaling, coupled to cyclic nucleotide
second messenger
MTNR1A, 4543G-protein signaling, coupled to cyclic nucleotide
second messenger
CNR2, 1269G-protein signaling, coupled to cyclic nucleotide
second messenger
HTR6, 3362G-protein signaling, coupled to cyclic nucleotide
second messenger
GRIK2, 2898glutamate signaling pathway
GRIN1, 2902glutamate signaling pathway
GRIN2A, 2903glutamate signaling pathway
GRIN2B, 2904glutamate signaling pathway
ADAM10, 102integrin-mediated signaling pathway
GRM7, 2917negative regulation of adenylate cyclase activity
LRP1, 4035negative regulation of Wnt receptor signaling
pathway
ADAM10, 102Notch receptor processing
ASCL1, 429Notch signaling pathway
HTR2A, 3356serotonin receptor signaling pathway
ADRB2, 154transmembrane receptor protein tyrosine kinase
activation (dimerization)
PTPRG, 5793transmembrane receptor protein tyrosine kinase
signaling pathway
EPHA4, 2043transmembrane receptor protein tyrosine kinase
signaling pathway
NRTN, 4902transmembrane receptor protein tyrosine kinase
signaling pathway
CTNND1, 1500Wnt receptor signaling pathway
Many of the genes are indeed related to Alzheimers Disease
through gamma secretase (presenilin) activityCourtesy of Susie
Stephens, Eli Lilly, Alan Ruttenberg, Science Commons, and the W3C
HCLS IG
The core
The core of the HCLS demo was the access/integration of public
datasets via the Semantic Web
Linking Open Data Project
Goal: expose open datasets in RDF
Set RDF links among the data items from different datasets
Set up SPARQL endpoints to query the data, too
Over 2 billion triples, 3 million links (November 2007)
Example data source: DBpedia
DBpedia is a community effort to
extract structured (infobox) information from Wikipedia
provide a SPARQL endpoint to the dataset
interlink the DBpedia dataset with other datasets on the Web
Extracting structured data from Wikipedia
http://en.wikipedia.org/wiki/Calgary
dbpedia:native_name Calgary ;
dbpedia:altitude 1048 ; dbpedia:population_city 988193 ;
dbpedia:population_metro 1079310 ;
dpbedia:mayor_name
dbpedia:Dave_Bronconnier ;
dpbedia:governing_body dbpedia:Calgary_City_Council ; ...
Courtesy of Chris Bizer, Free University of Berlin
Automatic links among open datasets
owl.sameAs ; ...
owl:sameAs wgs84_pos:lat 51.050112282; wgs84_pos:long
-114.085285152; sws:population 968460 ...DBpedia
Geonames
Processors can switch automatically from one to the other
Linking Open Data Project (cont)
This is a major community project
anybody can participate; to subscribe to the list:
http://simile.mit.edu/mailman/listinfo/linking-open-data
or look at the project site:
http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData
if you know of open data sets: contact the project to
incorporate it with the rest!
Applications using this set of data in real-life setting should
come to the fore soon
Examples of real applications
Integrate knowledge for Chinese Medicine
Integration of a large number of relational databases (on
traditional Chinese medicine) using a Semantic Layer
around 80 databases, around 200,000 records each
A visual tool to map databases to the semantic layer using a
specialized ontology
Form based query interface for end users
Courtesy of Huajun Chen, Zhejiang University, (SWEO Case
Study)
Find the right experts at NASA
Expertise locater for nearly 20,000 NASA civil servants using
RDF integration techniques over 6 or 7 geographically distributed
databases, data sources, and web services
Courtesy of Kendall Clark, Clark & Parsia, LLC
Public health surveillance (Sapphire)
Integrated biosurveillance system (biohazards, bioterrorism,
disease control, etc): integrates data from multiple disparate
sources and generated reports
Courtesy of Parsa Mirhaji, School of Health Information
Sciences, University of Texas (SWEO Case Study)
Different views of the data
New information can be added easily
She University of Texas Health Science Center (UTHSC) has used
Semantic Web technologies to build a prototype system for
context-aware interpretation and integration of clinical data,
environmental readings, and patient interviews known as Situational
Awareness and Preparedness for Public Health Incidences Using
Reasoning Engines (or SAPPHIRE). SAPPHIRE uses the Semantic Web to
enable public-health preparedness and allow for improved decision
making. UT-Houstons system integrates a wide range of health and
epidemiological data from local healthcare providers, hospitals and
pharmacies. SAPPHIRE constructs a collaborative and distributed
system to analyze, detect, and respond to public health matters. In
the greater Houston area, UTHSC has deployed SAPPHIRE to automate
this reporting process. Every ten minutes, SAPPHIRE receives
electronic health records, triage data, patients complaints, and
clinicians notes from eight hospitals spanning four counties and
covering more than 30% of all Houston-area emergency-room visits.
Using unstructured text analysis and Semantic Web technologies,
this information is mined and integrated into a single view of
current health conditions across the city.The flexibility of
Semantic Web technologies allows SAPPHIRE to operate equally
effectively in other contexts. At Hurricane Katrina Within eight
hours of the opening of the shelters, UTHSC configured SAPPHIRE to
respond to the needs of the disaster.
Oracles Technology Network portal
Courtesy of Mike DiLascio, Siderean Software, and Justin
Kestelyn, Oracle Corporation (SWEO Case Study)
Aggregates many source of content
Re-group, categorize, etccontent (using a taxonomy)
Integration of many information (from blogs, from databases,
etc) enriched with Oracle's own taxonomy, leads to a better user
interface and better search
Help for deep sea drilling operations
Integration of experience and data in the planning and operation
of deep sea drilling processes
Discover relevant experiences that could affect current or
planned drilling operations
Courtesy of David Norheim and Roar Fjellheim, Computas AS (SWEO
Use Case)
Additional paradigm: intelligent portals
Portals collecting data and presenting them to users
they can be public or behind corporate firewalls
Portals internal organization makes use of semantic data and
ontologies
integration with external and internal data
these applications often extend the data integration
paradigm
better queries,often based on controlled vocabularies or
ontologies
Semantic portal for art collections
Courtesy of Jacco van Ossenbruggen, CWI, and Guus Schreiber, VU
Amsterdam
The point to note:Taichang is the name of a Ming emperor. There
is nothing in the collection to match his name, However, there is
an extra information in the knowledge base that:
svcn:Ming_period skos:narrower svcn:Taichang_period
which means that artefacts of the Ming period are also
displayed
FAO Journal portal
Improved search on journal content based on an agricultural
ontology and thesaurus (AGROVOC)
Courtesy of Gauri Salokhe, Margherita Sini, and Johannes Keizer,
FAO, (SWEO Case Study)
Eli Lillys Target Assessment Tool
Better prioritization of possible drug target, integrating data
from different sources and formats
Integration, search, etc, via ontologies (proprietary and
public)
Courtesy of Susie Stephens, Eli Lilly (SWEO Case Study)
Digital music asset portal at NRK
Used by program production to find the right music in the
archive for a specific show
Courtesy of Robert Engels, ESIS, and Jon Roar Tnnesen, NRK (SWEO
Case Study)
NRK is the Norwegian National TV; currently 1.2 million tracks
are digitized and only 45,000 are used in practice; use a semantic
network search as a basic principle to find the others. Used by
program production
Improved Search via Ontology: GoPubMed
Improved search on top of pubmed.org
search results are ranked using ontologies
related terms are highlighted, usable for further search
Vodafone live!
Integrate various vendors product descriptions via RDF
ring tones, games, wallpapers
manage complexity of handsets, binary formats
A portal is created to offer appropriate content
Significant increase in content download after the
introduction
Courtesy of Kevin Smith, Vodafone Group R&D (SWEO Case
Study)
Other examples
Suns White Paper and System Handbook collections
Nokias S60 support portal
Harpers Online Magazine
Oracles virtual pressroom
Dow Jones Synaptica
All kind of other types of applications
The coming few examples are more knowledge management type of
applications
Less typical usage the Web aspect of the Semantic Web
Repair and Diagnostics Documentation
Store repair and diagnostic operations in a repository with an
OWL model
A diagnostic engine generates the manuals on the fly, using RDF
for information exchange
Courtesy of Franois-Paul Servant, Renault, (SWEO Use Case)
Portal to Principality of Asturias documents
Search through governmental documents
A bridge is created between the users and the juridical jargon
using SW vocabularies and tools
Courtesy of Diego Berrueta and Luis Polo, CTIC, U. of Oviedo,
and the Principality of Asturias, (SWEO Case Study)
Natural interface to business applications
Courtesy of C. Anantaram, Tata Consultancy Services Limited
(SWEO Case Study)
Users interact with a business application (eg, via email) in
natural language; OWL helps in the retrieval of relevant
concepts
Other application areas come to the fore
Content management
Knowledge management
Business intelligence
Collaborative user interfaces
Sensor-based services
Linking virtual communities
Grid infrastructure
Multimedia data management
Etc
Thank you for your attention!
These slides are publicly available on:
http://www.w3.org/2007/Talks/1119-Beijing-IH/
CEO guide for SW: the DO-s
Start small: Test the Semantic Web waters with a pilot project
[] before investing large sums of time and money.
Check credentials: A lot of systems integrators don't really
have the skills to deal with Semantic Web technologies. Get someone
who's savvy in semantics.
Expect training challenges: It often takes people a while to
understand the technology. []
Find an ally: It can be hard to articulate the potential
benefits, so find someone with a problem that can be solved with
the Semantic Web and make that person a partner.
Source: BusinessWeek Online, April 2007
CEO guide for SW: the DONT-s
Go it alone: The Semantic Web is complex, and it's best to get
help. []
Forget privacy: Just because you can gather and correlate data
about employees doesnt mean you should. Set usage guidelines to
safeguard employee privacy.
Expect perfection: While these technologies will help you find
and correlate information more quickly, theyre far from perfect.
Nothing can help if data are unreliable in the first place.
Be impatient: One early adopter at NASA says that the potential
benefits can justify the investments in time, money, and resources,
but there must be a multi-year commitment to have any hope of
success
Source: BusinessWeek Online, April 2007
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Ivan Herman, Semantic Web Adoption, CSWS2007, 2007-11-19,
Beijing, China ()
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