Interlinking the web of data: challenges and solutions

Transcription

1 Interlinking the web of data: challenges and solutions Work in collaboration with François Scharffe and others Jérôme Euzenat June 8, 2011

2 Outline ing Jérôme Euzenat Interlinking the web of data: challenges and solutions 2 / 38

3 Outline ing Jérôme Euzenat Interlinking the web of data: challenges and solutions 3 / 38

4 Linked (open) data cloud Jérôme Euzenat Interlinking the web of data: challenges and solutions 4 / 38

5 Publishing data on the web Four publication principles 1. Use URIs for identifying resources 2. Use dereferencable URIs 3. When a URI is dereferenced, a description of the identified resource is returned 4. Published datasets must be interconnected to other datasets If possible using semantic web technologies: URI, HTTP, RDF, OWL Jérôme Euzenat Interlinking the web of data: challenges and solutions 5 / 38

6 What for? and If you are an authority publishing data (gov), this allows you to put your data in the context other authority data and the others to reuse your data; If you are a link producer, i.e., someone who knows how to add value by cross-linking data, you have standard references to work with; If you are an application developer, e.g., seevl.net, you can take advantage of this data always on the web. the data is constantly up-to-date; the data can be freely linked; Data is browsable. Jérôme Euzenat Interlinking the web of data: challenges and solutions 6 / 38

7 Example: INSEE dataset Région table: code nom chef-lieu 11 Île-de-France Champagne-Ardenne Picardie Sous-région table: région département Jérôme Euzenat Interlinking the web of data: challenges and solutions 7 / 38

8 Example: Administrative ontology Territoire_FR code nom Pays Region subdivision chef-lieu Departement integer string Arrondissement Commune Jérôme Euzenat Interlinking the web of data: challenges and solutions 8 / 38

9 Example: RDF Pays rdf:type PAYS_FR i:code_iso FR i:nom i:sub i:sub France i:sub i:sub Region rdf:type i:code11 i:nom REG_11 i:chef-lieu Île-de-France DEP_78 DEP_77 rdf:type rdf:typerdf:type DEP_75 i:nom Paris i:nom COM_75056 rdf:type Departement Commune Jérôme Euzenat Interlinking the web of data: challenges and solutions 9 / 38

10 Example: Linking INSEE and NUTS NUTS: Nomenclature of territorial units for statistics #INSEE INSEE name NUTS Level #NUTS 1 Pays Région Département Arrondissement 4036 Canton Commune 5 Jérôme Euzenat Interlinking the web of data: challenges and solutions 10 / 38

11 Example: Linking INSEE and NUTS NUTS: Nomenclature of territorial units for statistics #INSEE INSEE name NUTS Level #NUTS 1 Pays Région Département Arrondissement 4036 Canton Commune 5 Å vs. Saint-Rémy-en-Bouzemont-Saint-Genest-et-Isson or Montbonnot Saint-Martin Jérôme Euzenat Interlinking the web of data: challenges and solutions 10 / 38

12 Example: Linking INSEE and NUTS Territoire_FR Pays PAYS_FR Region REG_11 Departement DEP_75 DEP_77 DEP_78 Commune COM_75056 owl:sameas owl:sameas owl:sameas owl:sameas owl:sameas Region Country FR1 UK1 NUTSRegion FR10 FR101 FR102 FR103 LAURegion Jérôme Euzenat Interlinking the web of data: challenges and solutions 11 / 38

13 Outline ing Jérôme Euzenat Interlinking the web of data: challenges and solutions 12 / 38

14 The data interlinking problem URI1 URI2 Jérôme Euzenat Interlinking the web of data: challenges and solutions 13 / 38

15 The data interlinking problem URI1 URI2 o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 13 / 38

16 The data interlinking problem URI1 URI2 Data interlinking o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 13 / 38

17 The data interlinking problem owl:sameas URI1 URI2 Data interlinking o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 13 / 38

18 Manual resource matching owl:sameas URI1 URI2 Manual observation Jérôme Euzenat Interlinking the web of data: challenges and solutions 14 / 38

19 Manual resource matching owl:sameas URI1 URI2 Manual observation This does not scale. Jérôme Euzenat Interlinking the web of data: challenges and solutions 14 / 38

20 URI matching owl:sameas URI1 URI2 URI transformation Jérôme Euzenat Interlinking the web of data: challenges and solutions 15 / 38

21 URI matching owl:sameas URI1 URI2 URI transformation owl:sameas Jérôme Euzenat Interlinking the web of data: challenges and solutions 15 / 38

22 URI matching owl:sameas URI1 URI2 URI transformation owl:sameas Jérôme Euzenat Interlinking the web of data: challenges and solutions 15 / 38

23 Data matching through a common ontology URI1 URI2 o Jérôme Euzenat Interlinking the web of data: challenges and solutions 16 / 38

24 Data matching through a common ontology URI1 URI2 Resource matching of datasets described by the same ontology o Jérôme Euzenat Interlinking the web of data: challenges and solutions 16 / 38

25 Data matching through a common ontology owl:sameas URI1 URI2 Resource matching of datasets described by the same ontology o Jérôme Euzenat Interlinking the web of data: challenges and solutions 16 / 38

26 Matching with a common ontology + Focus the search: only match instances of the same class; Not sufficient: it remains to identify corresponding entities + If keys are defined (OWL 2), this is done; + At least we know which properties to compare; Inferring secondary keys may be useful; Correcting discrepancies: record linkage. Jérôme Euzenat Interlinking the web of data: challenges and solutions 17 / 38

27 Record linkage Name Johann Date 1665 Place München Johannes Name 1667 Date Monaco di Bavaria Place Having a common ontology does not solve the problem. Jérôme Euzenat Interlinking the web of data: challenges and solutions 18 / 38

28 Data matching with different ontologies (implicit alignment) URI1 URI2 o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 19 / 38

29 Data matching with different ontologies (implicit alignment) URI1 URI2 Resource matching of datasets described by different ontologies o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 19 / 38

30 Data matching with different ontologies (implicit alignment) owl:sameas URI1 URI2 Resource matching of datasets described by different ontologies o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 19 / 38

31 Independent key computation Different span requires different key (France is not a key for INSEE); Differences in schema and depths makes difference in what is a key ("Paris" is both a department name (DEP_75) and a municipality name (COM_75056) for INSEE while the region name may be a key for NUTS) Keys are often meaningless: they are data-independent and database-dependent, hence they cannot be used for matching entities (REG_11 vs. FR_10). Jérôme Euzenat Interlinking the web of data: challenges and solutions 20 / 38

32 Independent key computation Different span requires different key (France is not a key for INSEE); Differences in schema and depths makes difference in what is a key ("Paris" is both a department name (DEP_75) and a municipality name (COM_75056) for INSEE while the region name may be a key for NUTS) Keys are often meaningless: they are data-independent and database-dependent, hence they cannot be used for matching entities (REG_11 vs. FR_10). rdf:type and insee:nom are keys for INSEE (Region); nuts:level and nuts:name are keys for NUTS (NUTSRegion); insee:nom corresponds to nuts:name; there exists a correspondence between rdf:type in INSEE and nuts:level. Jérôme Euzenat Interlinking the web of data: challenges and solutions 20 / 38

33 Data matching with different ontologies (explicit alignment) URI1 URI2 o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 21 / 38

34 Data matching with different ontologies (explicit alignment) URI1 URI2 o A o Jérôme Euzenat Interlinking the web of data: challenges and solutions 21 / 38

35 Data matching with different ontologies (explicit alignment) URI1 URI2 Resource matching of datasets described by different ontologies o A o Jérôme Euzenat Interlinking the web of data: challenges and solutions 21 / 38

36 Data matching with different ontologies (explicit alignment) owl:sameas URI1 URI2 Resource matching of datasets described by different ontologies o A o Jérôme Euzenat Interlinking the web of data: challenges and solutions 21 / 38

37 INSEE and NUTS: ontology alignment Territoire_FR code nom Pays Region subdivision chef-lieu Departement integer string Region name level code hassubregion Country NUTSRegion Arrondissement LAURegion Commune Jérôme Euzenat Interlinking the web of data: challenges and solutions 22 / 38

38 INSEE and NUTS: ontology alignment Territoire_FR code nom Pays Region subdivision chef-lieu Departement Arrondissement integer string = = Region name level code hassubregion Country NUTSRegion LAURegion Commune Jérôme Euzenat Interlinking the web of data: challenges and solutions 22 / 38

39 Ontology alignment + Ontology alignment restore the benefits of common ontology: it helps focussing the search; It is not exact science! (but alignments may be available); + Ontology alignment and data linking reinforce each others. Jérôme Euzenat Interlinking the web of data: challenges and solutions 23 / 38

40 A simple algorithm Find matching concepts [concept matching]; For each of them, determine matching properties based on the similarity between their values in both datasets [property matching]; From them find property combinations identifying corresponding entities [key extraction]; Link corresponding entities [link generation]. For instance, nom/region INSEE name/nutsregion NUTS and moreover they are unambiguous. Jérôme Euzenat Interlinking the web of data: challenges and solutions 24 / 38

41 Simple alignments are not sufficient Territoire_FR nom = Region name Region Departement NUTSRegion Commune Jérôme Euzenat Interlinking the web of data: challenges and solutions 25 / 38

42 Simple alignments are not sufficient Territoire_FR nom = Region name Region Departement DEP_75 nom Commune = = Paris = NUTSRegion FR101 name COM_75056 nom Jérôme Euzenat Interlinking the web of data: challenges and solutions 25 / 38

43 Simple alignments are not sufficient Territoire_FR nom = Region name Region Departement DEP_75 nom Commune = = = = Paris = NUTSRegion FR101 name COM_75056 nom Jérôme Euzenat Interlinking the web of data: challenges and solutions 25 / 38

44 Expressive alignments are necessary Region = FR1 = NUTSRegion 2 = level hasparentregion Jérôme Euzenat Interlinking the web of data: challenges and solutions 26 / 38

45 Expressive alignments are necessary Region subdivision = FR1 = = NUTSRegion 2 = level hasparentregion hassubregion Jérôme Euzenat Interlinking the web of data: challenges and solutions 26 / 38

46 Expressive alignments are necessary Region subdivision nom = FR1 = = = NUTSRegion 2 = level hasparentregion hassubregion name Jérôme Euzenat Interlinking the web of data: challenges and solutions 26 / 38

47 Query generation SELECT?r PREFIX insee: < PREFIX rdf: < FROM < WHERE {?r rdf:type insee:region. } SELECT?n PREFIX nuts: < PREFIX rdf: < FROM < WHERE {?n rdf:type nuts:nutsregion.?n nuts:level 2ˆˆxsd:int.?n nuts:hasparentregion nuts:fr1. } Jérôme Euzenat Interlinking the web of data: challenges and solutions 27 / 38

48 Query generation CONSTRUCT {?r owl:sameas?n. } PREFIX insee: < PREFIX nuts: < PREFIX rdf: < FROM < FROM < WHERE {?r rdf:type insee:region.?r insee:nom?l.?n rdf:type nuts:nutsregion.?n nuts:name?l.?n nuts:level 2ˆˆxsd:int.?n nuts:hasparentregion nuts:fr1. } Jérôme Euzenat Interlinking the web of data: challenges and solutions 28 / 38

49 Outline ing Jérôme Euzenat Interlinking the web of data: challenges and solutions 29 / 38

50 What does this mean? Ontology alignments are schema-level expression of correspondences; They are useful for focussing the search; Expressive alignments are necessary; They can be turned into SPARQL-based link generators. but it is also necessary to express instance level constraints: for converting data (e.g., mph vs. m/s); for expressing matching constraint on data (e.g., similarity). Jérôme Euzenat Interlinking the web of data: challenges and solutions 30 / 38

51 General framework URI1 URI2 o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 31 / 38

52 General framework URI1 URI2 o o Ontology matching Jérôme Euzenat Interlinking the web of data: challenges and solutions 31 / 38

53 General framework URI1 URI2 o A o Ontology matching Jérôme Euzenat Interlinking the web of data: challenges and solutions 31 / 38

54 General framework URI1 Data interlinking URI2 o A o Ontology matching Jérôme Euzenat Interlinking the web of data: challenges and solutions 31 / 38

55 General framework owl:sameas URI1 Data interlinking URI2 o A o Ontology matching Jérôme Euzenat Interlinking the web of data: challenges and solutions 31 / 38

56 Six interlinking tools RKB-CRS Co-reference resolution for the RKB knowledge base. LD-mapper Linking tool for the Music Ontology. ODD Linker SQL-based linker. RDF-AI Dataset linker and merger. Silk Linking script engine based on explicit linking description. KnoFuss Alignment based linker. and others: ObjectCoRef, LN2R, LIMES... Jérôme Euzenat Interlinking the web of data: challenges and solutions 32 / 38

57 Six interlinking tools owl:sameas URI1 URI2 o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 33 / 38

58 Six interlinking tools owl:sameas URI1 URI2 Data interlinking ODD-Linker, RKB-CRS, LD-Mapper o o Jérôme Euzenat Interlinking the web of data: challenges and solutions 33 / 38

59 Six interlinking tools owl:sameas URI1 URI2 Data interlinking ODD-Linker, RKB-CRS, LD-Mapper o A o Silk, RDF-AI Jérôme Euzenat Interlinking the web of data: challenges and solutions 33 / 38

60 Six interlinking tools owl:sameas URI1 URI2 Data interlinking ODD-Linker, RKB-CRS, LD-Mapper o A o Silk, RDF-AI Ontology matching KnoFuss Jérôme Euzenat Interlinking the web of data: challenges and solutions 33 / 38

61 Directions Combination between ontology matching and data interlinking; Explicit use of alignments or interlinking scripts; Data-driven algorithms (database key computation). Domain-specific techniques (geographic data). Jérôme Euzenat Interlinking the web of data: challenges and solutions 34 / 38

62 Outline ing Jérôme Euzenat Interlinking the web of data: challenges and solutions 35 / 38

63 A large part of linked data added value is in links; They may not be easy to find; Many techniques are available for automating interlinking; Having a general framework may help integrating them. Jérôme Euzenat Interlinking the web of data: challenges and solutions 36 / 38

64 Questions? Jerome. inria. fr