• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Steffen Staab's Presentation at SSSW 2011
 

Steffen Staab's Presentation at SSSW 2011

on

  • 1,228 views

 

Statistics

Views

Total Views
1,228
Views on SlideShare
672
Embed Views
556

Actions

Likes
0
Downloads
8
Comments
0

5 Embeds 556

http://sssw.org 549
http://sssw11.org 3
http://kmi-web06.open.ac.uk 2
http://207.46.192.232 1
http://www.sssw.org 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Steffen Staab's Presentation at SSSW 2011 Steffen Staab's Presentation at SSSW 2011 Presentation Transcript

    • Web Science & Technologies University of Koblenz ▪ Landau, Germany Provenance in the Semantic Web Steffen Staab Joint work withSimon Schenk, Renata Dividino, Christoph Ringelstein
    • Institut WeST – Web Science & TechnologiesSemantic Web Web Retrieval Interactive Web Multimedia Web Software Web eGovernment eMedia eScience eOrganizations ePerson Institute for Computer Institute for Leibniz Institute for Science Information Systems Social Sciences (GESIS) WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 2
    • Do you care where your data comes from?WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 3
    • How to loose 1,000,000,000 US$ in half a day WeSTVia @Bauckhage Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 4
    • +++ „Los Angeles (dpa) – In der kalifornischen Kleinstadt Bluewater soll es nach einem Bericht des örtlichen Senders vpk-tv zu einem Selbstmordanschlag gekommen sein. Es habe in einem Restaurant zwei Explosionen gegeben...“ +++ German Press Agency DPA, 10 Sep 2009WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 5
    • Guerilla MarketingWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 6
    • Loosing your reputation quickly… Hoax better check who said what when and whether you actually want to trust some informationWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 7
    • Defining ProvenanceProvenance … means the origin… of something, or the history of the ownership or location of an object. The term was …used for works of art, but is now … including science and computing. … In most fields, the primary purpose of provenance is to confirm or gather evidence as to the time, place, and— when appropriate—the person responsible for the creation, production, or discovery of the object. This will typically be accomplished by tracing the whole history of the object up to the present http://en.wikipedia.org/wiki/Provenance May 31, 2011WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 8
    • The situation… Call to Ontoprise from an insurance company: „Can you integrate our 5000 databases?“ EU IP experience (Large Engineering Company): „oh, we just found another PC that has several tens of thousands of relevant documents“ Linked open data cloudWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 9
    • Some of the problems… I have this piece of data. Can I actually believe it?  Default answer: Find some expert and ask him.  I have this inconsistency in my data. Who has introduced it and why?  Default answer: Try to find it in the sources.  I have this piece of data. How can I use it? Can I show it to anyone?  Default answer: • You are not allowed to do anything with it. Just throw it away. WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 10
    • Two Types of Provenance KnowledgeProvenance labels for facts Which confidence? Which privileges?Who? Bluewater is a City Which authority?When? WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 11
    • Two Types of Provenance KnowledgeProvenance labels for facts Open Provenance Model  RDF graph representing Which confidence?  Who did Which privileges? • whatWho? • when • why • … Bluewater is a City Which authority?  to a data item 1 2 3 4 5When? admission exami- asking exami- prepare nation permit nation share  „ex post“ workflow instance  audit/re-enact WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 12
    • SPARQL QUERYING USING PROVENANCE R. Dividino, S. Sizov, S. Staab, B. Schüler. Querying for Provenance, Trust, Uncertainty and other Meta Knowledge in RDF. In: Journal of Web Semantics. Elsevier, 7(3), 2009, pp. 204-219.WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 13
    • Representing Provenance Using URIshttp://bluewater.us a dbpedia:city.http://bluewater.us assertedBy http://neverest.deBUT:http://bluewater.us a dbpedia:fakecity.http://bluewater.us assertedBy http://dpa.deWho said what?WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 14
    • Representing Provenance Using URIs - 2http://neverest.de/bluewater a dbpedia:city.http://neverest.de/bluewater assertedBy http://neverest.dehttp://dpa.de/bluewater owl:sameAs http://neverest.de/bluewater.http://dpa.de/bluewater a dbpedia:fakecity.http:// dpa.de/bluewater assertedBy http://dpa.deWhat is the meaning of owl:same as now forprovenance?WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 15
    • Representing Provenance Using Named Graphshttp://dpa.de/ontology{ dbpedia:locatedIn rdf:domain dbpedia:company. dbpedia:locatedIn rdf:range dbpedia:city. …. }http://neverest.de/kb{ http://bluewater.us a dbpedia:city. http://vkptv.com a dbpedia:company. }http://dpa.de/provenance{ http://dpa.de/ontology dpa:lrm „2000-01-01“. http://dpa.de/ontology dpa:trust „highest“. http://neverest.de/kb dpa:lrm „2009-09-09“. http://neverest.de/kb dpa:trust „lowest“. }WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 16
    • Ambiguity in Representing Provenancehttp://neverest.de/kb{ http://bluewater.us a dbpedia:city. http://vkptv.com a dbpedia:company. }What does {…http://neverest.de/kb dpa:trust „lowest“. ..} mean? Distributive Reading Cumulative Reading  Each of the two facts is  Taken together the two assigned low trust facts are assigned low trustBoth readings are plausible under appropriate circumstances, but• Cumulative reading is harder to specify• Cumulative reading requires closing of sets of facts (contrast to RDF open world semantics) WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 17
    • Meta Knowledge: When?Meta knowledge dimension Set of values plus two operators  and  such that  and  are partial orders with a maximum D D , D DLeast Recently Modified DateL xsd:dateTime , L Ls.t. Total order,lrm(a L b) = max(lrm(a), lrm(b))  and  are dual operatorslrm(a L b) = min(lrm(a), lrm(b))WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 18
    • Meta Knowledge: Who?Meta knowledge dimension Set of values plus two operators  and  such that  and  are partial orders with a maximum D D , D DProvenanceP 2^SOURCES , P Ps.t.prov(a P b) = prov(a)  prov(b) Partial orderprov(a P b) = prov(a)  prov(b)  and  are same operatorWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 19
    • SPARQL: Algebraic Graph Query Languages [WWW08,SELECT ?city, ?broadcaster JoWS09]WHERE { ?city a ex:city. { ?broadcaster ex:activeIn ?city } UNION { ?broadcaster ex:locatedIn ?city }}WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 20
    • SPARQL: Algebraic Graph Query Languages [WWW08,SELECT ?city, ?broadcaster JoWS09]WHERE { 1 ?city a ex:city. 2 { ?broadcaster ex:activeIn ?city } UNION 3 { ?broadcaster ex:locatedIn ?city }}   2 3 1WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 21
    • SPARQL: Algebraic Graph Query LanguagesSELECT ?city, ?broadcaster [WWW08, JoWS09]WHERE { 1 2009-09-09 ?city a ex:city. 2 2009-09-09 { ?broadcaster ex:activeIn ?city } UNION 3 2009-09-08 { ?broadcaster ex:locatedIn ?city }}  |><| max  min 2 3 1 2009-09-08 2009-09-09 2009-09-09WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 22
    • OWL REASONING USING PROVENANCE S. Schenk, R. Dividino, S. Staab. Ontology Debugging Using Provenance. In: Journal of Web Semantics, Elsevier, accepted for publication.WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 23
    • Do we trust that bluewater is a real city?German Press Agency, Neverest,Highest trust, 2001-01-03 Low trust, 2009-09-09 WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 24
    • Explanation (Pinpointing)Given Ontology O, Axiom , O  OO is an explanation (pinpoint) for  wrt. O, iff O  and O*  for all O*  O12 Explanation formula ?3 ( 1  2 ) ( 3  4 )4WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 25
    • Finding Pinpoints O‘ OWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 26
    • Computation of meta knowledge for OWL Query: Meta Knowledge for  Compute Pinpointing Formula for  wrt O (A1  …  Am)  …  (Z1  …  Zn) Insert Meta Knowledge degrees and operators min(max(lrm(A1), …, lrm(Am)), max(lrm(Z1), …, lrm(Zn)) [KI 2009, Evaluate SWPM2009]WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 27
    • WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 28
    • „Least recently modified?“ (A1  …  Am)  …  (Z1  …  Zn)min(max(lrm(A1)),…, lrm(Am)),…,max(lrm(Z1),…,lrm(Zn)) WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 29
    • Optimization: Syntactic relevanceWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 30
    • Optimized Computation of Provenance 9 9 9 9 87 7 7 Time Order Oracle for you: relevant pinpoint 5 5 3 Syntactic Relevance 2 Color codes 2 2 reachability WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 31
    • Optimized Computation of Provenance 9 9 9 9 87 7 7 5 5 3 2 2 2 WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 32
    • Optimized Computation of Provenance 9 9 9 9 87 7 7 5 5 3 2 2 2 WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 33
    • Optimized Computation of Provenance 9 9 9 9 87 7 7 5 5 3 2 2 2 WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 34
    • Optimized Computation of Provenance 9 9 9 9 87 7 7 5 5 3 2 2 2 WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 35
    • Optimized Computation of Provenance 9 9 9 9 87 7 7 Relevant pinpoint only contained 5 5 3 2 2 2 WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 36
    • Evaluation: Computing Provenance in Milliseconds Real-world provenance! WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 37
    • PROVENANCE AWARE POLICY LANGUAGEWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 38
    • WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 39
    • Middle Rhine Hospital 1 admissionHealthRecord createPolicies createStickyLog create (P1): ukob is allowed to process health records for research purposes. However, ukob is not allowed to transfer the health records of patients to other organizations. (P2): The mrh demands that the record is only accessed by ukob after the sharing of the health records is approved by the patient and the approval must have been confirmed by a doctor. WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 40
    • Middle Rhine Hospital 1 2 3 4 5 6 exami- asking exami- prepare share for admission nation permit nation share researchHealthRecord create update update de-id. transferPolicies create update fulfill check transfer YouStickyLog create update update update update encrypt transfer Sticky Log: step (record, {mrh}, {}, create, patient_treatment, 1, {0}) step (record, {mrh}, {}, update, examination, 2, {1}) reduced (record, hidden, hidden, update, hidden, 4, {2}) step (record, {mrh}, {}, de-identified, privacy, 5, {4}) attribute (record, de-identified, true, 5) WeST step Steffen Staab {mrh}, {ukob}, transfer, research, 6, {5}) (record, Summer School Semantic Web staab@uni-koblenz.de 41
    • Middle Rhine Hospital 1 2 3 4 5 6 exami- asking exami- prepare share for admission nation permit nation share researchHealthRecord create update update de-id. transferPolicies create update fulfill check transfer You permit (6)?StickyLog create (P3): update update update update permit (ID) IF (step (record, _, _, transfer, _, ID, _) AND encrypt transfer attribute (record, de-identified, true, ID)). Sticky Log: step (record, {mrh}, {}, create, patient_treatment, 1, {0}) step (record, {mrh}, {}, update, examination, 2, {1}) reduced (record, hidden, hidden, update, hidden, 4, {2}) step (record, {mrh}, {}, de-identified, privacy, 5, {4}) attribute (record, de-identified, true, 5) WeST step Steffen Staab {mrh}, {ukob}, transfer, research, 6, {5}) (record, Summer School Semantic Web staab@uni-koblenz.de 42
    • Middle Rhine Hospital 1 2 3 4 5 6 exami- asking exami- prepare share for admission nation permit nation share researchHealthRecord create update update de-id. transferPolicies create update fulfill check transfer You permit (6)?StickyLog create (P3): update update update update permit (ID) IF (step (record, _, _, transfer, _, ID, _) AND encrypt transfer attribute (record, de-identified, true, ID)). Sticky Log: step (record, {mrh}, {}, create, patient_treatment, 1, {0}) step (record, {mrh}, {}, update, examination, 2, {1}) reduced (record, hidden, hidden, update, hidden, 4, {2}) step (record, {mrh}, {}, de-identified, privacy, 5, {4}) attribute (record, de-identified, true, 5) WeST step Steffen Staab {mrh}, {ukob}, transfer, research, 6, {5}) (record, Summer School Semantic Web staab@uni-koblenz.de 43
    • CONCLUSIONWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 44
    • Data Value lies in Past  Knowing what happened to your data  Knowing why it happened to your data Present  Drawing the right conclusions from your data Future  Deciding upon the destiny of your data Your Strategy is based on Provenance! You better take care!WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 45
    • Core References W3C working group: http://www.w3.org/2011/prov/wiki/Main_Page IEEE Internet Computing, Vol 15, Issue 1, Jan/Feb 2011 Special Issue on „Provenance in Web Applications“ http://www.computer.org/portal/web/csdl/abs/html/mags/ic/ 2011/01/mic2011010017.htm Journal of Web Semantics, Volume 9, Issue 2, 2011, Special Issue on „Provenance in the Semantic Web“ http://www.sciencedirect.com/science/journal/15708268 http://websemanticsjournal.orgWeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 46
    • Core References of Our Own WorkProvenance in RDF R. Dividino, S. Sizov, S. Staab, B. Schüler. Querying for Provenance, Trust, Uncertainty and other Meta Knowledge in RDF. In: Journal of Web Semantics. Special issue on "The Web of Data". Elsevier, 7(3), 2009, pp. 204-219.Provenance in OWL S. Schenk, R. Dividino, S. Staab, N. Kurz. Ontology Debugging Using Provenance. In: Journal of Web Semantics. Special issue on “Ontology Dynamics“, Elsevier, 9(3), 2011.Provenance for Policy Languages C. Ringelstein, S. Staab. Provenance-aware Policy Definition and Execution. In: IEEE Internet Computing, special issue on Provenance in Web Applications, Jan/Feb 2011, pp. 49-58.Capturing Provenance in Distributed Workflows C. Ringelstein, S. Staab. DiALog: A Distributed Model for Capturing Provenance and Auditing Information. International Journal of Web Services Research (JWSR), Idea Group Publishing, 7(2): 1-20, 2010.WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 47
    • Thank You! http://west.uni-koblenz.de See you again at…WeST Steffen Staab Summer School Semantic Web staab@uni-koblenz.de 48