exploiting large scale web semantics
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Exploiting Large Scale Web Semantics
Prof Enrico Motta, PhDKnowledge Media Institute
The Open UniversityMilton Keynes, UK
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The Semantic Web
<rdf:RDF><Feature rdf:about="http://sws.geonames.org/2638049/"><name>Shenley Church End</name><alternateName>Shenley</alternateName><inCountry rdf:resource="http://www.geonames.org/countries/#GB"/></rdf:RDF>
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The SW as a large scale source of knowledge
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Architecture of SW Apps
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The Knowledge Acquisition Bottleneck
Large Bodyof Knowledge
Intelligent Behaviour
KA Bottleneck
Knowledge
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SW as Enabler of Intelligent Behaviour
Intelligent Behaviour
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Example: Using the SW as background knowledge to support the alignment of
NALT and AGROVOC
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Proposal: • rely on online ontologies (Semantic Web) to derive mappings• ontologies are dynamically discovered and combined
A Brel
Semantic Web
Does not rely on any pre-selected knowledge sources.
M. Sabou, M. d’Aquin, E. Motta, “Using the Semantic Web as Background Knowledge inOntology Mapping", Ontology Mapping Workshop, ISWC’06. Best Paper Award
External Source = SW
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Strategy 1 - Definition
Find ontologies that contain equivalent classes for A and B and use their relationship in the ontologies to derive the mapping.
A Brel
Sem
anti
c W
eb
A1’B1’
A2’B2’
An’Bn’
O1
O2 On
BABA
BABA
BABA
BABA
''
''
''
''For each ontology use these rules:
…
These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’:
'''' BABCCA
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Strategy 1- Examples
Beef FoodS
eman
tic
Web
Beef
RedMeat
Tap
Food
MeatOrPoultry
SR-16 FAO_Agrovoc
ka2.rdf
Researcher AcademicStaff
Sem
anti
c W
eb
Researcher
AcademicStaff
ISWC SWRC
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Strategy 2 - Definition
BABCCAr
BABCCAr
BABCCAr
BABCCAr
BABCCAr
')5(
')4(
')3(
')2(
')1(
Principle: If no ontologies are found that contain the two terms then combine information from multiple ontologies to find a mapping.
A Brel
Sem
anti
c W
eb
A’BC
C’B’rel
rel
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.
CA 'CA '
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.
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Strategy 2 - Examples
PoultryChicken
FoodPoultry
Chicken Vs. Food(midlevel-onto)
(Tap)
Ex1:
FoodChicken
Ham Vs. FoodEx2:
(r1)
MeatHamFoodMeat
(pizza-to-go)
(SUMO) FoodHam
(Same results for Duck, Goose, Turkey)
(r1)
Ham Vs. SeafoodEx3:
MeatHamSeafoodMeat
(pizza-to-go)
(wine.owl) SeafoodHam (r3)
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Evaluation: 1600 mappings, two teams, 70% Precision
(derived from 180 different ontologies)
Matching AGROVOC (16k terms) and NALT(41k terms)
Large Scale Evaluation
M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “Exploiting the Semantic Web for Ontology Matching “. In Press
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Conclusions
• Our results with the NALT/AGROVOC matching problem show that the SW can be used effectively as a source of background knowledge for intelligent problem solving
• The SW provides an unprecedented opportunity to address the KA bottleneck and remove one of the fundamental barriers to the large-scale diffusion of knowledge-based intelligent systems
• This approach is being used in a number of other scenarios, including:– Semantic Web Browsing– Question Answering– Integration of Folksonomies with the SW
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