Weaving the Pedantic Web - Information Quality on the Web of Data

Transcription

1 Weaving the Pedantic Web - Information Quality on the Web of Data Andreas Harth Semantic Days Stavanger KIT University of the State of Baden-Württemberg and National Large-scale Research Center of the Helmholtz Association

2 Contents Introduction Data Integration Scenario Linked Data Quality Dimensions and Tools Conclusion 2

3 INTRODUCTION 3

4 Quality Fitness for use. Joseph Juran. The Quality Control Handbook. McGraw-Hill, New York, 3rd edition,

5 Data Quality Multi-faceted accurate = high quality? Availability? Timeliness? Task-dependent task: weather forecast data is not good if it is not available for online query vacation planning or aviation? Via Pablo N. Mendes, Giorgos Flouris, PlanetData WP2 5

6 Data Qualtiy and Task Complexity high Constraints Data quality/ Manual effort Schema Consolidation Object Consolidation low RDF/Linked Data Search/ Objec t Browsing Query (SPARQL) Reasoning (OWL) Complexity PageRank Link Prediction Trend Prediction 6

7 SCENARIO 7

8 Enterprise Data Integration vs. Web Data Integration?! User Interface Triple Store Object Consolidation Data available at Crawling Linked Data Interface 8

9 Queries List of oil platforms List of oil platforms in a particular region Companies that operate oil platforms in a particular region Directors of companies that operate oil platforms Regulatory filings of companies that operate oil platforms Region Filing Oil Platform Company Person 9

10 Wikipedia 10

11 USGS World Petroleum Assessment

12 Structurae.de 12

13 Brønnøysundregistrene 13

14 SEC Edgar 14

15 Enterprise Data Integration vs. Web Data Integration?! User Interface Triple Store Object Consolidation Crawling Linked Data Interface 15

16 TripleStore (CumulusRDF) Distributed deployment to enable scale (more data and also more clients) by adding more machines (via Cassandra) Geographical replication (via Cassandra) Write-optimized indices with eventual consistency (via Cassandra) Triple pattern lookups (via CumulusRDF index structures) Linked Data Lookups (via CumulusRDF index structures) SPARQL Query Processing (via Sesame query processor) 16

17 User Interface Keyword Search 17

18 User Interface Entity Browsing 18

19 User Interface Faceted Browsing 19

20 SPARQL Queries and Results 20

21 DATA QUALITY DIMENSIONS 21

22 Planet Data Quality Dimensions Via Pablo N. Mendes, Giorgos Flouris, PlanetData WP2 Category Intrinsic Dimensions Contextual Dimensions Representational Dimensions Accessibility Dimensions Dimension Accuracy Consistency Objectivity Timeliness Validity Believability Completeness Understandability Relevancy Reputation Verifiability Amount of Data Interpretability Rep. Conciseness Rep. Consistency Availability Response Time Security 22

23 Quality Assessment via Ranking Ranking of data in relational databases (ObjectRank): Domain expert provides schema authority graph manually Apply PageRank where schema authority graph encodes the strength of links Problem: web data uses tens of thousands of vocabulary terms Problem: anybody can say anything about anything on the web Solution: exploit a different level of abstraction to construct graph for PageRank calculation Step 1: construct data source graph Step 2: compute PageRank on data source graph Step 3: propagate rank of data sources to object URIs 23

24 Linked Data There is a syntactic relation between sources and identifiers, e.g. Tim belongs to Source B Any source can make a statement about any identifier Source A Jim knows Bob knows Source B Tim knows Mary Source C For example, is defined by 24

25 Step 1 Derive Naming Authority Matrix Source A references Bob so this counts as a vote from A to B Jim Tim Source B references Mary so this counts as a vote from B to C Source C references Tim so this counts as a vote from C to B Bob A C Mary B 25

26 Step 2 Compute PageRank on Naming Authority Matrix Run PageRank on the source graph to determine the trustworthiness of each source Resulting scores are indicated by node size A C B A B C 26

27 Step 3 Propagate Source Ranks to Identifiers Propagate source ranks to the nodes they reference Source A Source B Jim Jim Tim Mary A Tim Bob Source C B Bob C Mary 27

28 Quality Evaluation: User Study (11 participants, keyword query for own s name) 28

29 Data Quality Challenges in Integration Steps Constraints Schema Consolidation Object Consolidation RDF/Linked Data 29

30 The Pedantic Web Group Get the community to contact publishers about errors/issues as they arise Get involved: > 200 members! Acknowledgements to: Aidan Hogan, Alex Passant, Me, Antoine Zimmermann, Axel Polleres, Michael Hausenblas, Richard Cyganiak, Stéphane Corlosquet 30

31 Linked Data Validators Syntax errors quite rare, partly due to popularity of W3C RDF/XML syntax validator Need an all-in-one validation service Should not only validate strict errors, but give feedback on suspected issues We offer a prototypical service at: 31

32 Pedantic Web Frequently Observed Problems 1. Accessibility 1.1 Document Not Retrievable 1.2 Incorrect Content-Type 1.3 Content Negotiation for the Sake of Cleverness 1.4 Content Negotiation between Inappropriate Variants 1.5 Incorrect Interpretation of the Accept Header 1.6 Content Negotiation with Missing Vary Header 2. Parsing and Syntax 2.1 RDF/XML and RDFa: Ambiguous Base-URI 2.2 RDF/XML: rdf:id/rdf:nodeid/rdf:about/rdf:resource 3. Naming and Dereferencability 3.1 Redirects Other Than Datatype Literals 4.1 Malformed Datatype Literals 4.2 Incompatibility with Range Datatype 5. Reasoning 5.1 Bogus Values for Inverse-Functional Properties 5.2 Inconsistencies 32

33 Object Consolidation Links acorss datasets Interlinking frameworks such as Silk provide semi-automatic support for linking 33

34 Schema Consolidation Manual modelling effort required Ontology engineering tools such as Protégé, Neon Toolkit, TopBraid Composer 34

35 Linked Data Reasoning 35

36 Noisy Data: Omnipotent Being Proposition 1 Powerful reasoning on web/integrated data is dangerous. Proof: 08445a31a78661b5c746feff39a9db6e4e2cc5cf sha1-sum of mailto: common value for foaf:mbox_sha1sum Q.E.D. An inverse-functional (uniquely identifying) property!!! Any person who shares the same value will be considered the same 36

37 Authoritative Reasoning Consider source of schema data Class/property URIs dereference to their authoritative document FOAF spec authoritative for foaf:person MY spec not authoritative for foaf:person Allow extension in third-party documents my:person rdfs:subclassof foaf:person. (MY spec) BUT: Reduce obscure memberships foaf:person rdfs:subclassof my:person. (MY spec) ALSO: Protect specifications foaf:knows a owl:symmetricproperty. (MY spec) Aidan Hogan, Andreas Harth and Axel Polleres. "Scalable Authoritative OWL Reasoning for the Web ". Chapter in Semantic Services, Interoperability and Web Applications: Emerging Concepts, June 2011, IGI Global (invited republication). 37

38 CONCLUSION 38

39 Conclusion Information quality is an issue both inside enterprises and on the Web More sources leads to more diversity leads to less quality Lack of data quality requires manual effort for integration Pay-as-you-go integration Integration for basic search and retrieval effort is managable Object consolidation requires manual oversight Schema integration requires domain expertise and modelling skills Constraints require knowledge in logics Some automatic methods/heuristics to assuage noise in data exist 39