What You've Learned About Ontology Matching So Far

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

J´rˆme Euzenat
eo Ontology matching 2 / 36

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

J´rˆme Euzenat
eo Ontology matching 3 / 36 J´rˆme Euzenat

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.

J´rˆme Euzenat

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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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

J´rˆme Euzenat

Why should we deal with this? Applications: Query answering

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 First query reformulated query Second
mediator
Ontology evolution peer reformulated answer answer peer
Data interlinking

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Applications: Agent communication Data interlinking

First Second
Matcher
ontology ontology First Second
Matcher
ontology ontology
Alignment
Alignment

Generator
Transformed Generator
message ontology
Translator
Transformed message First Second
First Second links
dataset dataset
agent agent

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Ontology matching in three steps On what basis can we match?

Reconciliation can be performed in 3 steps o Content: relying on what is inside the ontology
o
Name, comments, alternate names, names of related entities: NLP, IR,
etc.
Match, Matcher Internal structure: constraints on relations, typing
External structure: relations between entities: Data mining, Discrete
thereby determines the alignment A mathematics
Extension: Statistics, data analysis, data mining, machine learning
Semantics (models): Reasoning techniques
Generate Generator
Context: the relations of the ontology with the outside
a processor (for merging, transforming, etc.) Transformation Annotated resources:
The web
External ontologies: dbpedia, etc.
Apply External resources: wordnet, etc.

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Name similarity Structure similarity

Monograph Monograph
Item pages Item integer pages
price isbn creator string isbn
title author author
DVD
doi title uri title
creator ≥ Essay Book Essay
pp
price
Person Literary critics Literary critics
DVD title
Human Politics doi Human Politics
Book pp
Biography Biography
author Writer author Person Writer
subject subject
Paperback Paperback
Autobiography Autobiography
Hardcover Hardcover
Literature Literature
CD CD

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Instance similarity Combining diﬀerent techniques

Monograph Basic matchers provide candidate correspondences, most of the systems use
Item several such matchers and further combine and ﬁlter their results.

o

Essay M A
Literary critics
DVD A A A
Politics
Book
Biography M A M A
Paperback
Autobiography o Matcher composition Aggregation Filtering
Hardcover
Bertrand Russell: My life Literature
CD Iteration
Albert Camus: La chute

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How well do these approaches work? Evaluation process

Ontology Alignment Evaluation Initiative (OAEI)

Formal comparative evaluation of diﬀerent ontology-matching tools;
Run every year since 2004; R
Variety of test cases (in size, in formalism, in content); o parameters m
evaluator
Results consistent across test cases;
Results very dependent on the tasks and the data (from under 50% of matching A
precision and recall to well over 80% if ontologies are relatively similar)
Progress every year! o resources

http://oaei.ontologymatching.org

Now involved in the SEALS (Semantics Evaluation At Large Scale) project.

J´rˆme Euzenat

Benchmark results (precision and recall Tools you should be aware of
curves)
1.
Frameworks
2010
ASMOV Alignment API: used by many tools; provides an exchange format and
2009 evaluation tools for OAEI. Alignment server for sharing.
Lily PROMPT (a Prot´g´ plug-in): includes a user interface and a plug-in
e e
2008 architecture.
precision

Lily COMA++: oriented toward database integration (many basic algorithms
implemented).
2007
ASMOV Matching systems
2006 OAEI best performers (Falcon, RiMOM, ASMOV, etc.)
RiMOM Available systems (FOAM, Falcon, COMA++, Aroma, etc.)
2005
Falcon

0. edna
0. recall 1.
J´rˆme Euzenat

The data interlinking problem Example: Linking INSEE and NUTS

owl:sameAs NUTS: Nomenclature of territorial units for statistics

#INSEE INSEE name NUTS Level #NUTS
URI1 URI2 1 Pays 0 34
1 142
26 R´gion
e 2 344
100 D´partement
e 3 1488
Data interlinking 342 Arrondissement
4036 Canton 4
52422 Commune 5
o o

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INSEE and NUTS: ontology alignment Simple alignments are not suﬃcient

Territoire FR Region
Territoire FR Region =
nom name
integer
code = name
nom string level Region
Pays ≤
code
Region = ≤
≤ hasSubRegion Departement NUTSRegion
subdivision ≤ =
chef-lieu Country DEP 75 ≤ FR101
≤ nom = = name
Departement ≤ NUTSRegion
=
Commune Paris
Arrondissement LAURegion
=
Commune COM 75056
nom

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Expressive alignments are necessary Query generation

SELECT ?r
PREFIX insee: <http://rdf.insee.fr/ontologie-geo-2006.rdf#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
FROM <http://rdf.insee.fr/geo/regions-2011.rdf>
NUTSRegion WHERE {
?r rdf:type insee:Region .
= 2 level
Region = }
=
FR1 hasParentRegion
= SELECT ?n
subdivision hasSubRegion
PREFIX nuts: <http://ec.europa.eu/eurostat/ramon/ontologies/geographic.rdf#
= PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
nom name
FROM <http://ec.europa.eu/eurostat/ramon/rdfdata/nuts2008/>
WHERE {
?n rdf:type nuts:NUTSRegion .
?n nuts:level 2^^xsd:int .
?n nuts:hasParentRegion nuts:FR1 .
}

J´rˆme Euzenat

Query generation What does this mean?

CONSTRUCT { ?r owl:sameAs ?n . }
PREFIX insee: <http://rdf.insee.fr/ontologie-geo-2006.rdf#>
PREFIX nuts: <http://ec.europa.eu/eurostat/ramon/ontologies/geographic.rdf#> Ontology alignments are schema-level expression of correspondences;
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> They are useful for focussing the search;
FROM <http://rdf.insee.fr/geo/regions-2011.rdf>
FROM <http://ec.europa.eu/eurostat/ramon/rdfdata/nuts2008/> Expressive alignments are necessary;
WHERE { They can be turned into SPARQL-based link generators.
?r rdf:type insee:Region .
?r insee:nom ?l . but it is also necessary to express instance level constraints:
?n rdf:type nuts:NUTSRegion . for converting data (e.g., mph vs. m/s);
?n nuts:name ?l . for expressing matching constraint on data (e.g., similarity).
?n nuts:level 2^^xsd:int .
?n nuts:hasParentRegion nuts:FR1 .
}

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General framework Selected challenges

Scalability and eﬃciency
Current matchers can be fast, scale and accurate, but not all at once.
owl:sameAs New sources of matching
Context-based matching,
URI1 URI2 General purpose matching (vs. special purpose matching)
Data interlinking Matcher combination,
Matcher selection and self-conﬁguration,
User involvement,
o A o Matching (serendipitously) while working,
How to explain alignments?
Social and collaborative ontology matching,
Alignment management: infrastructure and support,
Ontology matching
How do we maintain alignments when ontologies evolve?
Reasoning with alignments,
Being robust to incorrect alignments.
and, of course, many others,
J´rˆme Euzenat

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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What You've Learned About Ontology Matching So Far

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