1. What you have learned so far
Ontology matching
J´rˆme Euzenat
eo
Data can be expressed in RDF
Linked through URIs
Modelled with OWL ontologies
&
Retrieved through SPARQL queries
Montbonnot, France
Jerome.Euzenat@inrialpes.fr
Thanks to Pavel Shvaiko and Natasha Noy for our collaboration on former versions of these
slides
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Being serious about the semantic web Ontology heterogeneity
Monograph
Item integer pages
price isbn
It is not one person’s ontology string
title author
It is not several people common ontology doi title
creator uri
It is many people’s many ontologies pp Essay
So it is a mess, but a meaningful mess. Person Literary critics
DVD
Human Politics
Book
Biography
author Writer
Heterogeneity is not a bug, it is a feature subject
Paperback
Autobiography
Hardcover
Literature
CD
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2. Heterogeneity problem How can we address the problem?
Resources being expressed in different ways must be reconciled before being
used.
Mismatch between formalized knowledge can occur when:
different languages are used (OWL vs. Topic maps);
different terminologies are used: First ontology parameters
English vs. Chinese;
Book vs. Monograph. Initial alignment matching Resulting alignment
different models are used:
different classes: Autobiography vs. Paperback; Second ontology resources
classes vs. property: Essay vs. literarygenre;
classes vs. instances: One physical book as an instance vs. one work as
an instance.
different scopes and granularity are used.
Only books vs. cultural items vs. any product;
Books detailed to the print and translation level vs. books as works.
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Ontology alignment Expressive alignments (EDOAL)
≥ Monograph
integer Volume
Item pages Pocket ≥
price 14 size
string isbn
title ≥ author
doi title Book
creator uri =
Essay topic Autobiography
pp =
≥ Literary critics author
Person
DVD
≤ Human Politics
Book
Biography
author ≥ Writer
subject ∀x, Pocket(x) ⇐ Volume(x) ∧ size(x, y ) ∧ y ≤ 14
Paperback
Autobiography ∀x, Book(x) ∧ author (x, y ) ∧ topic(x, y ) ≡ Autobiography (x)
Hardcover
Literature
CD
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3. Transformation and mediation Ontology networks
a2 b5
SELECT x.doi SELECT x.isbn o2
WHERE x : Book WHERE x : Autobiography o5
AND x.author = ”Bertrand Russell” AND x.author = ”Bertrand Russell” b2 c2 A2,4 f5 g5
AND x.topic = ”Bertrand Russell” a4
A1,2
a1 f2 g2 d2 e2 h5 j5
o4
mediator
A2,3
o1 b4 c4
b1 c1 a3
f4 g 4 d4 e4
d1 e1 o3
b3 c3
A3,4
x.doi=http://dx.doi.org/10.1080/041522862X x.isbn=041522862X A1,3
f3 g3 d3 e 3
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Why should we deal with this? Application: Catalog integration
Applications of semantic integration
First Second
Matcher
Catalogue integration ontology ontology
Schema and data integration
Query answering Alignment
Peer-to-peer information sharing
Web service composition Generator
Agent communication
Data transformation Integrated
Catalog Transformation
Ontology evolution portal
Data interlinking
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4. Applications: Query answering Applications: Agent communication
First Second
First Second Matcher
Matcher ontology ontology
ontology ontology
Alignment
Alignment
Generator
Generator Transformed
message ontology
First query reformulated query Second Translator
mediator Transformed message
peer reformulated answer answer peer First Second
agent agent
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Data interlinking Ontology matching in three steps
Reconciliation can be performed in 3 steps o o
First Second
Matcher
ontology ontology
Match, Matcher
Alignment
thereby determines the alignment A
Generator Generate Generator
a processor (for merging, transforming, etc.) Transformation
First Second
links
dataset dataset Apply
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5. On what basis can we match? Name similarity
Monograph
Content: relying on what is inside the ontology Item pages
Name, comments, alternate names, names of related entities: NLP, IR, price isbn
etc. title author
Internal structure: constraints on relations, typing doi title
External structure: relations between entities: Data mining, Discrete creator ≥
mathematics pp Essay
Extension: Statistics, data analysis, data mining, machine learning Person Literary critics
Semantics (models): Reasoning techniques DVD
Human Politics
Context: the relations of the ontology with the outside Book
Biography
Annotated resources: author Writer
The web subject
Paperback
External ontologies: dbpedia, etc. Autobiography
External resources: wordnet, etc. Hardcover
Literature
CD
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Structure similarity Instance similarity
Monograph Monograph
Item integer pages Item
creator string isbn
author
DVD
uri title
Book Essay Essay
price
Literary critics Literary critics
title DVD
doi Human Politics Politics
pp Book
Biography Biography
author Person Writer
subject
Paperback Paperback
Autobiography Bertrand Russell: My life Autobiography
Hardcover Hardcover
Literature Albert Camus: La chute Literature
CD CD
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6. Combining different techniques How well do these approaches work?
Basic matchers provide candidate correspondences, most of the systems use Ontology Alignment Evaluation Initiative (OAEI)
several such matchers and further combine and filter their results.
o Formal comparative evaluation of different ontology-matching tools;
Run every year since 2004;
M A Variety of test cases (in size, in formalism, in content);
Results consistent across test cases;
A A A Results very dependent on the tasks and the data (from under 50% of
precision and recall to well over 80% if ontologies are relatively similar)
M A M A Progress every year!
o Matcher composition Aggregation Filtering
http://oaei.ontologymatching.org
Iteration
Now involved in the SEALS (Semantics Evaluation At Large Scale) project.
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Evaluation process Benchmark results (precision and recall
curves)
1.
2010
R ASMOV
2009
o parameters evaluator m Lily
2008
precision
matching A Lily
2007
ASMOV
o resources
2006
RiMOM
2005
Falcon
0. edna
0. recall 1.
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7. Tools you should be aware of Selected challenges
Scalability and efficiency
Current matchers can be fast, scale and accurate, but not all at once.
Frameworks New sources of matching
Context-based matching,
Alignment API: used by many tools; provides an exchange format and
evaluation tools for OAEI. Alignment server for sharing. General purpose matching (vs. special purpose matching)
PROMPT (a Prot´g´ plug-in): includes a user interface and a plug-in
e e Matcher combination,
architecture. Matcher selection and self-configuration,
COMA++: oriented toward database integration (many basic algorithms User involvement,
implemented). Matching (serendipitously) while working,
Matching systems How to explain alignments?
Social and collaborative ontology matching,
OAEI best performers (Falcon, RiMOM, ASMOV, etc.)
Available systems (FOAM, Falcon, COMA++, Aroma, etc.)
Alignment management: infrastructure and support,
How do we maintain alignments when ontologies evolve?
Reasoning with alignments,
Being robust to incorrect alignments.
and, of course, many others,
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Further reading
“Ontology Matching” by Euzenat and
Shvaiko Jerome.Euzenat@inria.fr
Proceedings of ISWC, ASWC, ESWC,
WWW conferences, etc.
Journal of web semantics, Semantic web http://exmo.inrialpes.fr
journal, Journal on data semantics, etc.
http://www.ontologymatching.org
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