Semantic Web research anno 2006: main streams, popular falacies, current status, future challenges

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Semantic Web research anno 2006main streams,
popular falacies, current status, future
challenges

• Frank van Harmelen
• Vrije Universiteit Amsterdam

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(No Transcript)
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This is a topical talkWebster referring
to the topics of the day, of temporary
interest
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Which Semantic Web are we talking about?
Semantic Web research anno 2006main streams,
popular falacies, current status, future
challenges
main streams
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General idea of Semantic Web

• Make current web more machine accessible(currentl
  y 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, data-integration
• Web Services
• Need semantic characterisations to find them
• Navigation
• by semantic proximity, not hardwired links
• .....

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General idea of Semantic Web(2)

• Do this by
• Making data and meta-dataavailable on the Webin
  machine-understandable form (formalised)
• Structure the data and meta-data in ontologies

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machine-understandable form (What its
like to be a machine)
META-DATA
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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)
• recipeexpose 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 2Enrichment of the current Web
• recipeAnnotate, 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 1Semantic Web as Web of Data

• Version 2Enrichment of the current Web

• Different use-cases
• Different techniques
• Different users

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Four popular falacies about the Semantic Web
Semantic Web research anno 2006main streams,
popular falacies, current status, future
challenges
popular falacies
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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 dont
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. Lots 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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The current state of Semantic Web
Semantic Web research anno 2006main streams,
popular falacies, current status, future
challenges
current status
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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 wheres 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(45k190k)
• 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
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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
• Tooling is rapidly emerging
• HP, IBM, Oracle, Adobe,
• Parsers,
• Editors,
• visualisers,
• large scale storage and querying
• Portal generation, search

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Where are we now applications

• healthy uptake in some areas
• knowledge management / intranets
• data-integration
• life-sciences
• convergence with Semantic Grid
• cultural heritage

• still very few applications in
• personalisation
• mobility/context awareness
• Most applications for companies, few
  applications for the public

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Future directions/challenges
Semantic Web research anno 2006main streams,
popular falacies, current status, future
challenges
future challenges
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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 forediting, storing,
  inferencing, visualising, etc
• except for two problems
• Learning
• Mapping
• Natural lang. technology is also done
• at least its 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
• data-integration
• search
• 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

Flexible extensible Metadata schemas
RDF