1. Language Technologies and the Semantic Web: An Essential Relationship.
Enrico Motta
Professor of Knowledge Technologies
Knowledge Media Institute
The Open University

2. Update on the Semantic Web
Content of the Talk
Update on the Semantic Web
Beyond the hype
What it is
Why it is interesting
What’s its status?
Semantic Web and AI
Semantic Web Applications
Key features
Reasoning on the Semantic Web
Key role of Language Technologies
Conclusions

3. The Semantic Web in 2 minutes…

6. <foaf:Person rdf:about="http://identifiers. kmi. open. ac
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<foaf:topic_interest>Knowledge Technologies</foaf:topic_interest>
<foaf:topic_interest>Semantic Web</foaf:topic_interest>
<foaf:topic_interest>Ontologies</foaf:topic_interest>
<foaf:topic_interest>Problem Solving Methods</foaf:topic_interest>
<foaf:topic_interest>Knowledge Modelling</foaf:topic_interest>
<foaf:topic_interest>Knowledge Management</foaf:topic_interest>
<foaf:based_near>
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<contact:nearestAirport>
<airport:name>London Luton Airport</airport:name>
<airport:iataCode>LTN</airport:iataCode>
<airport:location>Luton, United Kingdom</airport:location>
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<foaf:name>AquaLog</foaf:name>
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7. The foaf ontology

8. The SW as ‘Web of Data’

9. Current status of the semantic web
10-20 million semantic web documents
Expressed in RDF, OWL, DAML+OIL
7K-10K ontologies
These cover a variety of domains - multimedia, computing, management, bio-medical sciences, geography, entertainment, upper level concepts, etc…
The above figures refer to resources which are publicly accessible on the web

10. The Semantic Web today
To a significant extent the Semantic Web is already in place and is characterized by a widespread production of formalized knowledge models (ontologies and metadata), from a variety of different groups and individuals
“The Next Knowledge Medium - An information network with semi-automated services for the generation, distribution, and consumption of knowledge”
Stefik, 1986
“Knowledge modelling to become a new form of literacy?”
Stutt and Motta, 1997
Still primarily a research enterprise, however interest is rapidly increasing in both governmental and business organizations
“early adopters” phase
The result is slowly emerging as an unprecedented knowledge resource, which can enable a new generation of intelligent applications on the web

11. Semantic Web Applications
What can you do with the Semantic Web?

12. “Corporate Semantic Webs”
A ‘corporate ontology’ is used to provide a homogeneous view over heterogeneous data sources
Often tackle Enterprise Information Integration scenarios
Hailed by Gartner as one of the key emerging strategic technology trends
E.g., see personal information management in Garlik

14. Exploiting large scale semantics
Next Generation SW Applications
Semantic Web
Semantic Web
Gateway

15. Exploiting large scale semantics
Next Generation SW Applications
Semantic Web

16. NGSW Applications in the context of AI research

17. Knowledge-Based Systems
“Today there has been a shift in paradigm. The fundamental problem of understanding intelligence is not the identification of a few powerful techniques, but rather the question of how to represent large amounts of knowledge in a fashion that permits their effective use”
Goldstein and Papert, 1977
Large Body of Knowledge
Intelligent Behaviour

18. The Knowledge Acquisition Bottleneck
Large Body of Knowledge KA
Bottleneck
Intelligent Behaviour

19. SW as Enabler of Intelligent Behaviour
Both a platform for knowledge publishing and a large scale source of knowledge
Intelligent Behaviour

20. KBS vs SW Systems Classic KBS SW Systems Provenance Centralized
Distributed
Size
Small/Medium
Extra Huge
Repr. Schema
Homogeneous
Heterogeneous
Quality
High
Very Variable
Degree of trust

21. Key Paradigm Shift
Classic KBS
SW Systems
Intelligent Behaviour
A function of sophisticated, logical, task-centric problem solving
A side-effect of being able to integrate different types of reasoning to handle size and heterogeneous quality and representation

22. Next Generation SW Applications: Examples
Case Study 1: Automatic Alignment of Thesauri in the Agricultural/Fishery Domain

23. SCARLET - matching by Harvesting the SW
Method
SCARLET - matching by Harvesting the SW
Automatically select and combine multiple online ontologies to derive a relation
Access
Semantic Web
Scarlet
Deduce
Concept_A
(e.g., Supermarket)
Concept_B
(e.g., Building)
Semantic Relation
( )

24. Two strategies Scarlet Scarlet Supermarket Building Cholesterol
OrganicChemical
PublicBuilding
Lipid
Shop
Steroid
Steroid
Supermarket
Cholesterol
Semantic Web
Scarlet
Scarlet
Supermarket
Building
Cholesterol
OrganicChemical
(A)
(B)
Deriving relations from (A) one ontology and (B) across ontologies.

25. Experiment Matching: AGROVOC UN’s Food and Agriculture
Organisation (FAO) thesaurus
descriptor terms
non-descriptor terms
NALT
US National Agricultural
Library Thesaurus
descriptor terms
non-descriptor terms

26. 226 Used Ontologies http://139.91.183.30:9090/RDF/VRP/Examples/tap.rdf
htechsight/Technologies.daml

27. Evaluation 1 - Precision
Manual assessment of 1000 mappings (15%)
Evaluators:
Researchers in the area of the Semantic Web
6 people split in two groups
Results:
Comparable to best results for background knowledge based matchers.

28. Evaluation 2 – Error Analysis

29. Other Case Studies…

30. Giving meaning to tags

31. Example
Cluster_1: {college commerce corporate course education high instructing learn learning lms school student}
education
training1,4
qualification
corporate1
institution
university2,3
college2
postSecondary
School2
school2
student3
studiesAt
course3
offersCourse
takesCourse
activities4
learning4
teaching4
1http://gate.ac.uk/projects/htechsight/Employment.daml.
2http://reliant.teknowledge.com/DAML/Mid-level-ontology.daml.
3http://
4http://

35. Conclusions

36. Typical misconceptions…
“The SW is a long-term vision…”
Ehm…actually… it already exists…
“The SW will never work because nobody is going to annotate their web pages”
The SW is not about annotating web pages, the SW is a web of data, most of which are generated from DBs, or from web mining software, or from applications which produce SW data as a side effect of supporting users’ tasks
“The idea of a universal ontology has failed before and will fail again. Hence the SW is doomed”
The SW is not about a single universal ontology. Already there are around 10K ontologies and the number is growing…
SW applications may use 1, 2, 3, or even hundreds of ontologies.

37. SW and Language Technologies
All the applications mentioned here combine language, web, statistical and semantic technologies
Heterogeneity and sloppy modelling implies that language and statistical technologies are almost always needed when building NGSW apps
In contrast with traditional KBS, intelligent behaviour is more a side-effect of intg. multiple techniques to handle scale and heterogeneity, rather than a function of powerful deductive reasoning