Semantic Web research
anno 2006:
main streams, popular falacies,
current status, future challenges
Frank van Harmelen
Vrije Universiteit Amsterdam
This is NOT
a Semantic Web
evangelization talk
(I assume
you are already
converted)
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This is a “topical” talk:
Webster:
“referring to the topics of the day,
of temporary interest”
Semantic Web research anno 2006:
main
mainstreams
streams, popular falacies,
current status, future challenges
Which Semantic Web
are we talking about?
General idea of Semantic Web
Make current web more machine accessible
(currently all the intelligence is in the user)
Motivating use-cases
 Search engines
• concepts, not keywords
• semantic narrowing/widening of queries
 Shopbots
• semantic interchange, not screenscraping
 E-commerce

Negotiation, catalogue mapping, personalisation

Need semantic characterisations to find them
 Web Services
 Navigation
• by semantic proximity, not hardwired links
 .....
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General idea of Semantic Web(2)
Do this by:
1. Making data and meta-data
available on the Web
in machine-understandable form
(formalised)
2. Structure the data and meta-data in
These are non-trivial
ontologies
design decisions.
Alternative would be:
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“machine-understandable form”
(What it’s like to be a machine)
alleviates
META-DATA
<treatment>
<name>
<symptoms>
IS-A
<drug>
<drug
administration>
<disease>
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Expressed using the W3C stack
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Which Semantic Web?
Version 1:
"Semantic Web as Web of Data" (TBL)
recipe:
expose databases on the web,
use RDF, integrate
meta-data from:

expressing DB schema semantics
in machine interpretable ways
enable integration and unexpected re-use
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Which Semantic Web?
Version 2:
“Enrichment of the current Web”
recipe:
Annotate, classify, index
meta-data from:

automatically producing markup:
named-entity recognition,
concept extraction, tagging, etc.
enable personalisation, search, browse,..
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Which Semantic Web?
Version 1:
“Semantic Web as Web of Data”
Version 2:
“Enrichment of the current Web”
 Different use-cases
 Different techniques
 Different users
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Semantic Web research anno 2006:
main streams, popular
popular falacies
falacies,
current status, future challenges
Four popular falacies
about the Semantic Web
First: clear up some popular
misunderstandings
False statement No :
“Semantic Web people try to
enforce meaning from the top”
They only “enforce” a language.
They don’t enforce what is said in that language
Compare: HTML “enforced” from the top,
But content is entirely free.
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First: clear up some popular
misunderstandings
False statement No :
“The Semantic Web people will require
everybody to subscribe to a single predefined
"meaning" for the terms we use.”
Of course, meaning is fluid, contextual, etc.
Lot’s of work on (semi)-automatically
bridging between different vocabularies.
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First: clear up some popular
misunderstandings
False statement No :
“The Semantic Web will require users to
understand the complicated details of
formalised knowledge representation.”
All of this is “under the hood”.
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First: clear up some popular
misunderstandings
False statement No :
“The Semantic Web people will require us to
manually markup all the existing web-pages.”
Lots of work on automatically producing
semantic markup:
named-entity recognition,
concept extraction, etc.
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Semantic Web research anno 2006:
main streams, popular falacies,
current
current status
status, future challenges
The current state of
Semantic Web
4 hard questions on the
Semantic Web:
Q1: "where does the meta-data come from?”
 NL technology is delivering on concept-extraction
 Socially emerging (learning from tagging).
Q2: “where do the meta-data-schema
come from?”
 many handcrafted schema
 hierarchy learning remains hard
 relation extraction remains hard.
Q3: “what to do with many meta-data schema?”
 ontology mapping/aligning remains VERY hard.
Q4: “where’s the ‘Web’ in the Semantic Web?”
 more attention to social aspects (P2P, FOAF)
 non-textual media remains hard
 deal with typical Web requirements.
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Q1: Where do the ontologies
come from?
Professional bodies, scientific communities,
companies, publishers, ….
Good old fashioned Knowledge Engineering
Convert from DB-schema, UML, etc.
Learning remains very hard…
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Q1: Where do the ontologies
come from?
 handcrafted

music: CDnow (2410/5), MusicMoz (1073/7)
 community efforts

biomedical: SNOMED (200k), GO (15k),
 commercial: Emtree(45k+190k)
 ranging from lightweight (Yahoo)
to heavyweight (Cyc)
 ranging from small (METAR)
to large (UNSPC)
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Q2: Where do the annotations
come from?
- Automated learning
- shallow natural language analysis
- Concept extraction
Example: Encyclopedia Britannica on “Amsterdam”
trade
antwerp
europe
amsterdam
merchant
netherlands
center
city
town
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Q2: Where do the annotations
come from?
 lightweight NLP

Dutch language semantic search engine
 exploit existing legacy-data


Amazon
Lab equipment
 side-effect from user interaction

MIT Lab photo-annotator
 NOT from manual effort
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Q3: What to do with many
ontologies?
 Mesh


Medical Subject Headings, National Library of Medicine
22.000 descriptions
 EMTREE


Commercial Elsevier, Drugs and diseases
45.000 terms, 190.000 synonyms
 UMLS

Integrates 100 different vocabularies
 SNOMED

200.000 concepts, College of American Pathologists
 Gene Ontology

15.000 terms in molecular biology
 NCI Cancer Ontology:

17,000 classes (about 1M definitions),
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Q3: What to do with many
ontologies?
Stitching all this together by hand?
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Q3: What to do with many
ontologies?
Linguistics & structure
Shared vocabulary
Instance-based matching
Shared background knowledge
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Where are we now: tools
Languages are stable (W3C)
Tooling is rapidly emerging






HP, IBM, Oracle, Adobe, …
Parsers,
Editors,
visualisers,
large scale storage and querying
Portal generation
Aduna
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Applications of the Semantic Web?
 healthy uptake in some areas:
 knowledge management / intranets
 data-integration
 life-sciences
 convergence with Semantic Grid
 cultural heritage
 very few applications in
 personalisation
 mobility/context awareness
 Most applications for companies,
few applications for the public
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Semantic Web research anno 2006:
main streams, popular falacies,
current status, future
future challenges
challenges
Future
directions/challenges
Semantic Web as an integrator
of many different subfields
Databases
Natural Language Processing
Knowledge Representation
Machine Learning
Information Retrieval
Agents
HCI
….
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Provocation…
Ontology research is done……


We know how to
make, maintain & deploy them
We have tools & methods for
editing, storing, inferencing, visualising, etc
… except for two problems:


Learning
Mapping
Natural lang. technology is also done…

at least it’s good enough
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Large open questions
 Ontology learning & mapping
 emerging semantics (social & statistical)
 Semantic Web services

discovery, composition: realistic?
 non-textual media

the semantic gap: text or social?
 Deployment:
1. data-integration
2. search
3. personalisation
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Changing focus
centralised,
formalised,
complete,
precise
distributed,
heterogeneous,
open, P2P,
approximate,
lightweight
Web 3.0 = Web 2.0 + Semantic Web
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Predicting the future…
Slide by Carol Goble
Artificial Intelligence
Decision making
OWL
Lots
SWRL
Ontology
Building
Not
much
Knowledge
Discovery
Flexible &
extensible
Metadata
schemas
Semantic Information
Web
linking
Services
NLP
FOAF
RDF
Social
RSS
bookmarking
Collective Intelligence
Not
much
Web
Lots
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