1. A Research Programme for the Semantic Web
Enrico Motta, Sofia Angeletou, Claudio Baldassarre, Mathieu D’Aquin, Martin Dzbor, Laurian Gridinoc, Davide Guidi, Vanessa Lopez, Marta Sabou
Knowledge Media Institute The Open University Milton Keynes, UK

2. We call the resulting applications:
Introduction
This talk presents a number of projects, which are part of an integrated effort at exploring the possibilities opened by the Semantic Web, viewed as a domain-independent, large scale supplier of formally encoded background knowledge, with respect to enabling intelligent problem solving.
We call the resulting applications:
Next Generation Semantic Web Applications

3. Organization of the Talk
The Semantic Web
The Semantic Web in the context of AI research
Next Generation Semantic Web Applications
What are they?
Why are they different from 1st generation SW Applications?
Examples
Ontology Matching
Integrating Web2.0 and SW
Semantic Web Browsing
Question Answering
Conclusions

4. The Semantic Web
The collection of all formal, machine processable, web accessible, ontology-based statements (semantic metadata) about web resources and other entities in the world, expressed in a knowledge representation language based on an XML syntax (e.g., OWL, DAML, DAML+OIL, RDF, etc…)

6. Semantic Web Document
<foaf:PersonalProfileDocument rdf:about="
<dc:title>Sofia Angeletou&apos;s RDF Description</dc:title>
<rdfs:label>Sofia Angeletous RDF Description</rdfs:label>
<dc:description>RDF description for Sofia Angeletou in machine-readable RDF/XML</dc:description>
<dc:creator rdf:resource=" />
<foaf:maker rdf:resource="
<foaf:primaryTopic rdf:resource="
</foaf:PersonalProfileDocument>
<foaf:Person rdf:about="
<foaf:name>Sofia Angeletou</foaf:name>
<foaf:firstName>Sofia</foaf:firstName>
<foaf:surname>Angeletou</foaf:surname>
<foaf:mbox_sha1sum>F78114D4E45CFC6AC811E6191F50182FB </foaf:mbox_sha1sum>
<foaf:phone rdf:resource="tel:+44-(0) "/>
<foaf:homepage rdf:resource="
<foaf:publications rdf:resource="
<foaf:workplaceHomepage rdf:resource="
<foaf:workInfoHomepage rdf:resource="
<foaf:depiction rdf:resource="

7. Charting the web

8. Charting the web (2)

9. Increasing Semantic Content
<rdf:RDF>
<Feature rdf:about="
<name>Shenley Church End</name>
<alternateName>Shenley</alternateName>
<inCountry rdf:resource="
</rdf:RDF>

10. The Rise of Semantics

11. Thesis #1
The SW today has already reached a level of scale good enough to make it a very useful source of knowledge to support intelligent applications
In other words: the Semantic Web is no longer an aspiration but a reality
The availability of such large scale amounts of formalised knowledge is unprecedented in the history of AI

12. Thesis #2
The SW may well provide a solution to one of the classic AI challenges: how to acquire and manage large volumes of knowledge to develop truly intelligent problem solvers and address the brittleness of traditional KBS

13. Knowledge Representation Hypothesis in AI
Any mechanically embodied intelligent process will be comprised of structural ingredients that
we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and
independent of such external semantic attribution, play a formal but causal and essential role in engendering the behaviour that manifests that knowledge
Brian Smith, 1982

14. Intelligence as a function of possessing domain knowledge
Large Body of Knowledge KA
Bottleneck
Intelligent Behaviour

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

17. SW as Enabler of Intelligent Behaviour

18. Overall Goal
Our research programme is to contribute to the development of this large-scale web of data and develop a new generation of web applications able to exploit it to provide intelligent functionalities

19. Architecture of NGSW Apps
Semantic Web
Gateway

21. Issue: Semantic Web Infrastructure

22. Current Gateway to the Semantic Web

23. Limitations of Swoogle
Limited quality control mechanisms
Many ontologies are duplicated
Limited Query/Search mechanisms
Only keyword search; no distinction between types of elements
No support for formal query languages (such as SPARQL)
Limited range of ontology ranking mechanisms
Swoogle only uses a 'popularity-based' one
Limited API
No support for ontology modularization

24. A New Gateway to the Semantic Web

25. Sophisticated quality control mechanism
Detects duplications
Fixes obvious syntax problems
E.g., duplicated ontology IDs, namespaces, etc..
Structures ontologies in a network
Using relations such as: extends, inconsistentWith, duplicates
Provides sophisticated API
Supports formal queries (SPARQL)
Variety of ontology ranking mechanisms
Modularization support
Very cool logo!

26. Examples of Next Generation Semantic Web Applications

27. Example #1: Ontology Matching

28. Ontology Matching 1 0.9 0.5 Label similarity methods
e.g., Full_Professor = FullProfessor
0.5
Structure similarity methods
Using taxonomic/property related information

29. New paradigm: use of background knowledge
(external source) R A’ B’ R A B

30. External Source = One Ontology
Aleksovski et al. EKAW’06
Map (anchor) terms into concepts from a richly axiomatized domain ontology
Derive a mapping based on the relation of the anchor terms
Assumes that a suitable (rich, large) domain ontology (DO) is available.

31. External Source = Web van Hage et al. ISWC’05
rely on Google and an online dictionary in the food domain to extract semantic relations between candidate terms using IR techniques
+ OnlineDictionary
Does not rely on a rich Domain Ont,
IR Methods
Precision increases significantly if domain specific sources are used:
50% - Web;
75% - domain texts.
rel A B

32. rely on online ontologies (Semantic Web) to derive mappings
External Source = SW
Proposal:
rely on online ontologies (Semantic Web) to derive mappings
ontologies are dynamically discovered and combined
Semantic Web
Does not rely on any pre-selected knowledge sources.
rel A B
M. Sabou, M. d’Aquin, E. Motta, “Using the Semantic Web as Background Knowledge in Ontology Mapping", Ontology Mapping Workshop, ISWC’06. Best Paper Award

33. How to combine online ontologies to derive mappings?

34. Strategy 1 - Definition
Find ontologies that contain equivalent classes for A and B and use their relationship in the ontologies to derive the mapping.
For each ontology use these rules:
B1’
B2’
Bn’
Semantic Web …
An’
A1’
A2’ O2 On O1
These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’:
rel A B

35. Strategy 1- Examples Beef Food Semantic Web RedMeat Tap MeatOrPoultry
SR-16
FAO_Agrovoc
ka2.rdf
Researcher
AcademicStaff
Semantic Web
ISWC
SWRC

36. Strategy 2 - Definition
Principle: If no ontologies are found that contain the two terms then combine information from multiple ontologies to find a mapping.
Details:
(1) Select all ontologies containing A’ equiv. with A
(2) For each ontology containing A’:
(a) if find relation between C and B.
(b) if find relation between C and B.
Details:
(1) Select all ontologies containing A’ equiv. with A
(2) For each ontology containing A’:
(a) if find relation between C and B.
(b) if find relation between C and B.
rel B’ C’
Semantic Web
rel C B A’
rel A B

37. Strategy 2 - Examples (Same results for Duck, Goose, Turkey) Ex1: Vs.
(midlevel-onto)
(Tap)
(Same results for Duck, Goose, Turkey)
Ex2:
Vs.
(pizza-to-go)
(r1)
(SUMO)
Ex3:
Vs.
(pizza-to-go)
(r3)
(wine.owl)

38. Large Scale Evaluation
Matching AGROVOC (16k terms) and NALT(41k terms)
(derived from 180 different ontologies)
Evaluation: 1600 mappings, two teams
Overall performance comparable to best in class
M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “Improving Ontology Matching by Dynamically Exploring Online Knowledge“. In Press

39. Chart 2

40. Thesis #3
Using the SW to provide dynamically background knowledge to tackle the Agrovoc/NALT mapping problem provides the first ever test case in which the SW, viewed as a large scale heterogeneous resource, has been successfully used to address a real-world problem

41. Example #2: Integrating SW and Web2.0

42. Features of Web2.0 sites Tagging as opposed to rigid classification
Dynamic vocabulary does not require much annotation effort and evolves easily
Shared vocabulary emerge over time
certain tags become particularly popular

43. Limitations of tagging
Different granularity of tagging
rome vs colosseum vs roman monument
Flower vs tulip
Etc..
Multilinguality
Spelling errors, different terminology, plural vs singular, etc…
This has a number of negative implications for the effective use of tagged resources
e.g., Search exhibits very poor recall

44. Giving meaning to tags

45. What does it mean to add semantics to tags?
1. Mapping a tag to a SW element
"japan" <akt:Country Japan>
2. Linking two "SW tags" using semantic relations
{japan, asia} <japan subRegionOf asia>

46. Applications of the approach
To improve recall in keyword search
To support annotation by dynamically suggesting relevant tags or visualizing the structure of relevant tags
To enable formal queries over a space of tags
Hence, going beyond keyword search
To support new forms of intelligent navigation
i.e., using the 'semantic layer' to support navigation

47. Filter infrequent tags
Pre-processing
Tags
Group similar tags
Filter infrequent tags
Concise tags
Clean tags
Folksonomy
Clustering
Analyze co-occurrence of tags
Co-occurence matrix
Cluster tags
Cluster1
Cluster2 …
Clustern
Concept and relation identification
Yes
2 “related” tags
SW search engine
Remaining
tags?
Wikipedia
Find mappings & relation for pair of tags No
Google
END
<concept, relation, concept>

48. Experiments Scope: Subsets of Flickr and del.icio.us tags.
Pre-processing (thresholds):
To be “similar”, Levenshtein >= 0.83;
A tag has to occur at least 10 times.
Total
Distinct
# entries
# tags
# users
# resources
del.icio.us
19,605
89,978
7,164
14,211
11,960
Flickr
49,087
167,130
6,140
17,956
Total
Distinct
# entries
# tags
# users
# resources
del.icio.us
18,882
70,194
7,090
13,579
1,265
Flickr
44,032
127,098
5,321
2,696

49. Examples
Cluster_1: {admin application archive collection component control developer dom example form innovation interface layout planning program repository resource sourcecode}
participant
innovation
event
developer
activity
creator
planning
example
applica-
tion
user
admin
resource
typeRange
component
interface
partici-
patesIn
in-event
archive
Information Object
has-mention-of

50. Examples
Cluster_2: {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://

51. Approach proven to be feasible and promising. However…
Lessons Learnt
Approach proven to be feasible and promising.
However…
Assumptions in initial experiments (e.g., single ontology coverage for pairs of tags; focus on classes, clustering-based approach, etc..) too restrictive
Swoogle is too limited to support a fully automated approach
we are now using Watson for the current experiments
Integration with SW-enabled ontology matching algorithm is essential to improve term matching

52. Example #3: Semantics-Enhanced Web Browsing

53. Introduce aspl
Magpie-based
on-the-fly annotation + contextualised services
Supporting students making sense of semantic web research

59. Conclusions
SW provides an unprecedented opportunity to build a new generation of intelligent systems, able to exploit large scale background knowledge
The large scale background knowledge provided by the SW may address one of the fundamental premises (and holy grails) of AI
The SW is not an aspiration: it is a concrete technology that is already in place today and is steadily becoming larger and more robust
The new class of systems enabled by the SW is fundamentally different in many respects both from traditional KBS and even from early SW applications
The examples shown in this talk provide an initial taste of the new generation of applications which will be made possible by the emerging Semantic Web

60. References Watson Ontology Mapping
Lopez, V., Sabou, M., Motta, E. (2006). "Mapping the real semantic web on the fly". ISWC 2006
Sabou, M., D'Aquin, M., Motta, E. (2006). "Using the semantic web as background knowledge for ontology mapping". ISWC 2006 Workshop on Ontology Mapping.
Integration of Web2.0 and Semantic Web
L.Specia, E. Motta, "Integrating Folksonomies with the Semantic Web", ESWC 2007.
Angeletou, S., Sabou, M., Specia, L., and Motta, E., (2007). “Bridging the Gap Between Folksonomies and the Semantic Web: An Experience Report”. ESWC 2007 Workshop on Bridging the Gap between Semantic Web and Web 2.0.
Watson
d’Aquin, M., Sabou, M., Dzbor, M., Baldassarre, C., Gridinoc, L., Angeletou, S. and Motta, E.: "WATSON: A Gateway for the Semantic Web". Poster Session at ESWC 2007

61. 'Vision' Papers
Motta, E., Sabou, M. (2006). "Next Generation Semantic Web Applications". 1st Asian Semantic Web Conference, Beijing.
Motta, E., Sabou, M. (2006). "Language Technologies and the Evolution of the Semantic Web". LREC 2006, Genoa, Italy.
Motta, E. (2006). "Knowledge Publishing and Access on the Semantic Web: A Socio-Technological Analysis". IEEE Intelligent Systems, Vol.21, 3, (88-90).