Ontology matching tutorial J er ome Euzenat Provide an - - PowerPoint PPT Presentation

Ontology matching tutorial

J´ erˆ

• me Euzenat

& Laboratoire d’Informatique de Grenoble Montbonnot, France Jerome.Euzenat@inria.fr http://exmo.inria.fr

Thanks to Pavel Shvaiko & Natasha Noy

Problem Applications Methods OM & FCA Conclusions

Goals of the tutorial

◮ Provide an introduction to ontology matching; ◮ . . . and eventually the semantic web; ◮ Start the discussion on links with formal concept analysis

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Outline

1

Problem

2

Applications

3

Methods

4

Ontology matching and FCA

5

Conclusions

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The semantic web?

The semantic web is an effort for publishing formal knowledge on the web. It has developed various languages: RDF Expressing data as graphs; OWL, RDFS Expressing the ontologies governing such graphs; SPARQL Query language for such graph GRDDL, RDFa Embedding knowledge on the web There are many tools for dealing with such languages and many resources expressed through it.

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Problem Applications Methods OM & FCA Conclusions

The semantic web is a success!

Such technologies are used every day (by yourself). ◮ Tens of billions of RDF triples and thousands of ontologies on the web; ◮ Governments and their agencies publish their data in RDF; ◮ Facebook (OG), Google (GKG), Yandex, Yahoo, Microsoft (schema.org) produce and consume semantic markup. ◮ And you do not even have to notice it.

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What is an ontology?

An ontology typically provides a vocabulary that describes a domain of interest and a specification of the meaning of terms used in the vocabulary. Depending on the precision of this specification, the notion of ontology encompasses several data and conceptual models, including, sets of terms, classifications, thesauri, database schemas, or fully axiomatized theories.

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Semantic webs

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Being serious about the semantic web

◮ It is not one guy’s ontology. ◮ It is not several guys’ common ontology. ◮ It is many guys and girls’ many ontologies. ◮ So it is a mess, but a meaningful mess.

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Problem Applications Methods OM & FCA Conclusions

Living with heterogeneity

The semantic web will be: ◮ huge, ◮ dynamic, ◮ heterogeneous. These are not bugs, these are features. We must learn to live with them and master them.

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The heterogeneity 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:

◮ English vs. Chinese; ◮ Book vs. Volume.

◮ different models are used:

◮ different classes: Autobiography vs. Paperback; ◮ 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 matching

Product Book DVD CD title creator ratings price sales doi topic author integer string uri Person Volume Essay Literary critics Politics Biography Autobiography Literature isbn author title subject Human Writer Bertrand Russell: My life Albert Camus: La chute ⊒ ⊒ ⊒ ⊒ ⊑ = =

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Transformation and mediation

SELECT ?i WHERE { ?x rdf:type o’:Autobiography . ?x o’:author/o’:name ”Bertrand Russell” . ?x o’:isbn ?i .}

mediator

SELECT ?d WHERE {?x rdf:type o:Book . ?x o:creator ?y . ?x o:topic ?y . ?y o:name ”Bertrand Russell” . ?x o:doi ?d .} x.doi=http://dx.doi.org/10.1080/041522862X x.isbn=041522862X

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Problem Applications Methods OM & FCA Conclusions

Correspondences and alignments

Definition (Correspondence)

Given two ontologies o and o′, a correspondence between o and o′ is a 3-uple: e, e′, r such that: ◮ e and e′ are entities of o and o′, for instance, classes, XML elements; ◮ r is a relation, for instance, equivalence (=), more general (⊒), disjointness (⊥).

Definition (Alignment)

Given two ontologies o and o′, an alignment (A) between o and o′: ◮ is a set of correspondences between o and o′ ◮ with some additional metadata (multiplicity: 1-1, 1-*, method, date, . . .)

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Terminology: a summary

Matching is the process of finding relationships or correspondences between entities of different ontologies. Alignment is a set of correspondences between two or more (in case of multiple matching) ontologies. The alignment is the output of the matching process. Correspondence is the relation supposed to hold according to a particular matching algorithm or individual, between entities of different

• ntologies.

Mapping is the oriented version of an alignment.

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The matching process

• ′

matching A′ A parameters resources

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Why should we deal with this?

Applications of ontology matching: ◮ Catalogue integration ◮ Schema and data integration ◮ Query answering ◮ Peer-to-peer information sharing ◮ Web service composition ◮ Agent communication ◮ Data transformation ◮ Ontology evolution ◮ Data interlinking

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Problem Applications Methods OM & FCA Conclusions

Applications: catalog integration

DB

• ′

DBPortal Matcher A Generator Translator

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Applications: ontology evolution

Kbt

• t
• t+n

Matcher A Generator Transformation Kbt+n

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Application: Data interlinking

First dataset First

• ntology

Second dataset Second

• ntology

Matcher Alignment Generator links

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Applications requirements

Application instances run time automatic correct complete

• peration

Ontology evolution √ √ √ transformation Schema integration √ √ √ merging Catalog integration √ √ √ data translation Data integration √ √ √ query answering Linked data √ √ data interlinking P2P information sharing √ query answering Web service composition √ √ √ data mediation Multi agent communication √ √ √ √ data translation Query answering √ √ query reformulation

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Problem Applications Methods OM & FCA Conclusions

On reducing heterogeneity

Reconciliation can be performed in 2 steps

• ′

Match, Matcher thereby determine an alignment A Generate Generator a processor (for merging, transforming, etc.) Transformation Matching can be achieved at run time or at design time.

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On what basis can we match?

◮ Content: relying on what is inside the ontology

◮ Name, comments, alternate names, names of related entities: NLP, IR, etc. ◮ Internal structure: constraints on relations, typing ◮ External structure: relations between entities: data mining, discrete mathematics ◮ Extension: statistics, data analysis, data mining, machine learning ◮ Semantics (models): reasoning techniques

◮ Context: the relations of the ontology with the outside

◮ Annotated resources: ◮ The web ◮ External ontologies: dbpedia, etc. ◮ External resources: wordnet, etc.

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Name similarity

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

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Structure similarity

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

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Problem Applications Methods OM & FCA Conclusions

Instance similarity

Item DVD Book Paperback Hardcover CD Volume Essay Literary critics Politics Biography Autobiography Literature Bertrand Russell: My life Albert Camus: La chute

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Basic methods: extensional

ǫ : C → E E can be a set of instances, a set of documents which are indexed by concepts, a set of items, e.g., people, which use these concepts. Two cases: ◮ E is common to both ontologies; ◮ E depends on the ontology. This can be reduced to the former case by identification or record linkage techniques. Techniques: ◮ statistical and machine learning techniques infer and compare the characteristics of populations; ◮ set-theoretic techniques compare the extensions;

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Extensional techniques

Product DVD Book CD Monograph Essay Literary critics Politics Biography Autobiography Literature ≥ ≥ . 8

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Extensional techniques

Product DVD Book CD Monograph Essay Literary critics Politics Biography Autobiography Literature ≥ ≥ . 8

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Problem Applications Methods OM & FCA Conclusions

Ontology matching and FCA

Ontology matching: ◮ From concepts, individuals and features of two sources ◮ Find equivalent concepts, features (and individuals) Formal concept analysis: ◮ Form individuals and features ◮ Find concepts

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What is the same/what is different

◮ two sides (with no correspondences) instead of one ◮ the goal is not to create concepts

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Rough idea

all B1 F1 G1 C1 D1 E1 ⊥ all B2 G2 H2 C2 D2 E2 F2 ⊥

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Rough idea

all B1 F1 G1 C1 D1 E1 ⊥ all B2 G2 H2 C2 D2 E2 F2 ⊥ ⊑

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Problem Applications Methods OM & FCA Conclusions

Encoding OM into FCA

◮ Really need to have common instances:

◮ data interlinking (see tomorrow talk) ◮ any other technique (which is equivalent)

◮ what can be the features:

◮ classes (in both ontologies) ◮ properties/relations

The problem is that the result will not be much different from cardinality analysis (concepts will be pairs of classes for which cardinality is 100%).

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FCA-Merge [Stumme and M¨ adche, 2001]

• 1. instance extraction (→ create common instances);
• 2. compute lattice (FCA);
• 3. interactive merge of the ontologies (comparing classes covering concept

extent and deciding to merge them).

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Summary

◮ Heterogeneity of ontologies is in the nature of the semantic web; ◮ Ontology matching is part of the solution; ◮ It can be based on many different techniques; ◮ There are already numerous systems around; ◮ A relatively solid research field has emerged (tools, formats, evaluation, etc.) and it keeps making progress; ◮ But there remain serious challenges ahead.

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Challenges

◮ Large-scale and efficient matching, ◮ Matching with background knowledge, ◮ Matcher selection, combination and tuning, ◮ User involvement, ◮ Social and collaborative matching, ◮ Uncertainty in matching, ◮ Reasoning with alignments, ◮ Alignment management. and, of course, many others...

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Problem Applications Methods OM & FCA Conclusions

Acknowledgments

We thank all the participants of the Heterogeneity workpack- age of the Knowledge Web network of excellence In particular, we are grateful to Than-Le Bach, Jesus Barrasa, Paolo Bouquet, Jan De Bo, Jos De Bruijn, Rose Dieng-Kuntz, Enrico Franconi, Ra´ ul Garc´ ıa Castro, Manfred Hauswirth, Pascal Hitzler, Mustafa Jarrar, Markus Kr¨

• tzsch, Ruben Lara, Malgorzata Mochol, Amedeo Napoli, Luciano

Serafini, Fran¸ cois Sharffe, Giorgos Stamou, Heiner Stuckenschmidt, York Sure, Vojtˇ ech Sv´ atek, Valentina Tamma, Sergio Tessaris, Paolo Traverso, Rapha¨ el Troncy, Sven van Acker, Frank van Harmelen, and Ilya Zaihrayeu. And more specifically to Marc Ehrig, Fausto Giunchiglia, Loredana Laera, Diana Maynard, Deborah McGuinness, Petko Valchev, Mikalai Yatskevich, and Antoine Zimmermann for their support and insightful comments Part of this work was carried out while Pavel Shvaiko was with the University of Trento.

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Ontology matching the book, 2nd edition

J´ erˆ

• me Euzenat, Pavel Shvaiko

Ontology matching

• 1. Applications
• 2. The matching problem
• 3. Methodology
• 4. Classification
• 5. Basic similarity measures
• 6. Global matching methods
• 7. Strategies
• 8. Systems
• 9. Evaluation
• 10. Representation
• 11. User involvement
• 12. Processing

http://book.ontologymatching.org

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Thank you for your attention and interest! Jerome.Euzenat@inria.fr Pavel.Shvaiko@infotn.it http://www.ontologymatching.org

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