Implicit and Explicit Schema Information

Transcription

1 Scalable Extraction of Implicit and Explicit Schema Information in Linked Open Data Ansgar Scherp Data and Web Science, U Mannheim Matthias Konrath, Thomas Gottron Web Science and Technologies, U Koblenz Slide 1

2 Linked Data We witness a major movement in the Web Publishing and interlinking of data of different quality, purpose and source on the Web Technology + Social Phenomenon World Wide Web Linked Data Documents Data Hyperlinks Typed Links HTML RDF Addresses (URIs) Addresses (URIs) Slide 2

3 Relevance of Linked Data? Slide 3

4 Linked Data: May 07 Æ Sept. 11 Web 2.0 Media Publications egovernment Cross-Domain Geographic Ansgar Scherp Triples < 31 Billion Life Sciences Slide 4 Source:

5 Linked Data: Based on Four Principles 1. Identification 2. Interlinkage 3. Dereferencing 4. Description Slide 5

6 1. Use URIs for Identification Matt Briggs gg Scott Miller people/matt-briggs people/scott-miller people/scott miller B. Gazen, CC-BY Slide 6

7 2. Interlinking of Ressources people/matt-briggs people/scott miller people/scott-miller B. Gazen, CC-BY Slide 7

8 3. Dereferencing of URIs Pretty easy to look up web documents How do we look up things of the real world? people/matt-briggs.rdf Slide 8

9 4. Description of URIs foaf:person dp:birmingham rdf:type foaf:based_near biglynx:matt-briggs ex:loc foaf:knows _1:point biglynx:scott-miller wgs84: lat foaf:based_near dp:london wgs84: long Slide 9

10 Searching for Data Sources Persons that are - Politicians and - Actors? Scherp ansgar@informatik.uni-mannheim.de <Ansgar 31 Milliarde Triples Slide 10 Quelle:

11 Searching for Data Sources (2) No single federated query interface provided Schema-based index to find data sources Index? Query SELECT?x FROM WHERE {?x rdf:type ex:actor.?x rdf:type ex:politician. } Slide 11

12 Idea Schema-based index Define families of graph patterns Assign instances to graph patterns Store the source information (context URI) Construction o Stream-based for scalability Little loss of accuracy Slide 12

13 Input Data n-quads Example: <subject> <predicate> <object> <context> < < yp < < webdav/timbl/foaf.rdf w3p: #me foaf: Person Slide 13

14 Layer 1: RDF Classes All instances of a C 1 particular type DS 1 DS 2 DS 3 SELECT?x FROM WHERE {?x rdfs:type foaf:person. } foaf:person timbl: card#i foaf: Person Slide 14

15 Layer 2: Type Clusters C C All instances belonging 1 2 to exactly the same set TC of types 1 SELECT?x FROM WHERE {?x rdfs:type foaf:person.?x rdfs:type pim:male. } DS 1 DS 2 DS 3 foaf:person pim:male tc4711 timbl: card#i pim: Male foaf: Person Slide 15

16 Layer 3: Equivalence Classes Two instances are C 1 C 2 C equivalent iff: They are in the same TC TC 1 They have the same p properties The property targets are EQC 1 in the same TC DS 1 DS 2 DS 3 C 3 TC 1 TC 2 Similar to 1-bisimulation Slide 16

17 Layer 3: Equivalence Classes eqc0815 -maker- tc1234 SELECT?x WHERE {?x rdfs:type foaf:person.?x rdfs:type pim:male.?x foaf:maker?y.?y rdfs:type foaf:personalprofiledocument. } pim: Male foaf: Person foaf: PPD foaf:person tc4711 eqc0815 pim:male foaf:ppd tc1234 foaf:maker a timbl: card#i timbl: card p Slide 17

18 Building the Schema and Index C1 C2 C3 Ck RDF classes consistsof Type TC1 TC2 TCm clusters haseq Class p1 p2 EQC1 EQC2 EQCn Equivalence classes hasdatasource DS1 DS2 DS3 DS4 DS5 DSx Data sources Slide 18

19 Building the Index from a Stream Stream of n-quads (coming from a LD crawler) Q16, Q15, Q14, Q13, Q12, Q11, Q10, Q9, Q8, Q7, Q6, Q5, Q4, Q3, Q2, Q1 C3 FiFo 4 C2 3 C2 C Linear runtime complexity wrt # of input triples Slide 19

20 Computing SchemEX: TimBL Data Set Analysis of a smaller data set 11 M triples, TimBL s FOAF profile LDspider with ~ 2k triples / sec Different cache sizes: 100, 1k, 10k, 50k, 100k Compared SchemEX with reference schema Index queries on all Types, TCs, EQCs Good precision/recall ratio at 50k+ Slide 20

21 Quality of Stream-based Index Construction Runtime increases hardly with window size Memory consumption scales with window size Commodity hardware (4GB RAM, single CPU) Slide 21

22 Computing SchemEX: Full BTC 2011 Data Cache size: 50 k Slide 22

23 Billion Triple Challenge 2011 [JWS 12] Slide 23

24 Did you mean? Result Set Size Ranked Retrieval Result Snippets Related Queries Slide 24