Problem Applications Methods OM & FCA Conclusions Goals of the tutorial Ontology matching tutorial J´ erˆ ome Euzenat ◮ Provide an introduction to ontology matching; ◮ . . . and eventually the semantic web; & ◮ Start the discussion on links with formal concept analysis Laboratoire d’Informatique de Grenoble Montbonnot, France Jerome.Euzenat@inria.fr http://exmo.inria.fr Thanks to Pavel Shvaiko & Natasha Noy Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 2 / 42 Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions Outline The semantic web? The semantic web is an effort for publishing formal knowledge on the web. Problem 1 It has developed various languages: Applications 2 RDF Expressing data as graphs; OWL, RDFS Expressing the ontologies governing such graphs; Methods 3 SPARQL Query language for such graph GRDDL, RDFa Embedding knowledge on the web Ontology matching and FCA 4 There are many tools for dealing with such languages and many resources Conclusions 5 expressed through it. Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 3 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 5 / 42
Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions The semantic web is a success! What is an ontology? Such technologies are used every day (by yourself). An ontology typically provides a vocabulary that describes a domain of ◮ Tens of billions of RDF triples and thousands of ontologies on the web; interest and a specification of the meaning of terms used in the vocabulary. ◮ Governments and their agencies publish their data in RDF; Depending on the precision of this specification, the notion of ontology ◮ Facebook (OG), Google (GKG), Yandex, Yahoo, Microsoft encompasses several data and conceptual models, including, sets of terms, (schema.org) produce and consume semantic markup. classifications, thesauri, database schemas, or fully axiomatized theories. ◮ And you do not even have to notice it. Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 6 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 7 / 42 Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions Semantic webs 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. Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 8 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 9 / 42
Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions Living with heterogeneity 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); The semantic web will be: ◮ different terminologies are used: ◮ huge, ◮ English vs. Chinese; ◮ dynamic, ◮ Book vs. Volume. ◮ heterogeneous. ◮ different models are used: These are not bugs, these are features. ◮ different classes: Autobiography vs. Paperback; ◮ classes vs. property: Essay vs. literarygenre; We must learn to live with them and master them. ◮ 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. Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 10 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 11 / 42 Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions Ontology matching Transformation and mediation SELECT ?d SELECT ?i ⊒ WHERE { ?x rdf:type o:Book . WHERE { ?x rdf:type o’:Autobiography . Product integer Volume ?x o:creator ?y . ?x o’:author/o’:name ”Bertrand Russell” . title isbn ⊒ ?x o:topic ?y . string ?x o’:isbn ?i . } creator author ?y o:name ”Bertrand Russell” . ratings title uri ?x o:doi ?d . } price Essay sales ⊒ ⊑ Literary critics doi Person topic Human Politics mediator Book Biography = = author Writer ⊒ subject DVD Bertrand Russell: My life Autobiography CD Literature Albert Camus: La chute x.doi=http://dx.doi.org/10.1080/041522862X x.isbn=041522862X Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 12 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 13 / 42
Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions Correspondences and alignments Terminology: a summary Definition (Correspondence) Given two ontologies o and o ′ , a correspondence between o and o ′ is a Matching is the process of finding relationships or correspondences 3-uple: � e , e ′ , r � such that: between entities of different ontologies. ◮ e and e ′ are entities of o and o ′ , for instance, classes, XML elements; Alignment is a set of correspondences between two or more (in case of ◮ r is a relation, for instance, equivalence (=), more general ( ⊒ ), multiple matching) ontologies. The alignment is the output of disjointness ( ⊥ ). the matching process. Correspondence is the relation supposed to hold according to a particular Definition (Alignment) matching algorithm or individual, between entities of different Given two ontologies o and o ′ , an alignment ( A ) between o and o ′ : ontologies. ◮ is a set of correspondences between o and o ′ Mapping is the oriented version of an alignment. ◮ with some additional metadata (multiplicity: 1-1, 1-*, method, date, . . . ) Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 14 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 15 / 42 Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions The matching process Why should we deal with this? Applications of ontology matching: ◮ Catalogue integration ◮ Schema and data integration o parameters ◮ Query answering ◮ Peer-to-peer information sharing matching A ′ A ◮ Web service composition resources o ′ ◮ Agent communication ◮ Data transformation ◮ Ontology evolution ◮ Data interlinking Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 16 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 18 / 42
Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions Applications: catalog integration Applications: ontology evolution o Matcher o ′ o t o t + n Matcher A A Generator Generator DB Translator DBPortal Kb t + n Kb t Transformation Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 19 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 20 / 42 Problem Applications Methods OM & FCA Conclusions Problem Applications Methods OM & FCA Conclusions Application: Data interlinking Applications requirements automatic operation instances complete run time correct First Second Matcher ontology ontology Application √ √ √ Ontology evolution transformation Alignment √ √ √ Schema integration merging √ √ √ Catalog integration data translation √ √ √ Data integration query answering Generator √ √ Linked data data interlinking √ P2P information sharing query answering √ √ √ First Second Web service composition data mediation links √ √ √ √ dataset dataset Multi agent communication data translation √ √ Query answering query reformulation Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 21 / 42 Ontology matching tutorial (v17): CLA-2016 (Moskow, Russia) – Euzenat and Shvaiko 22 / 42
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