Reflections on  Provenance Ontology Encodings  Li Ding 1 , Jie Bao 1 , James Michaelis 1 , Jun Zhao 2 , Deborah L. McGuinn...
From Provenance Vocabulary to Provenance Ontology <ul><li>Provenance Vocabulary </li></ul><ul><ul><li>Metadata Only </li><...
Semantic Web Provenance Ontologies <ul><li>Selection Criteria </li></ul><ul><ul><li>Declarative: encoded in OWL and RDFS  ...
Examples: OPM, PML and DCTerms Source: http://dublincore.org/documents/2001/11/30/dcq-rdf-xml/ Source: OPM specification v...
Basic Statistics  <ul><li>Small ontology  </li></ul><ul><li>Within OWL/OWL2 DL expressivity </li></ul><ul><li>Not tractabl...
Semantic Analysis <ul><li>What can be learned from these OWL encodings of provenance ontology? </li></ul><ul><li>Concept C...
Empirically Identified Themes (5W+H) <ul><li>We empirically identify  themes  to group provenance primitives </li></ul><ul...
Theme Coverage Analysis <ul><li>Similarity </li></ul><ul><li>Difference </li></ul><ul><ul><li>Agents:  Immutable (OPM1.1),...
Computational Model <ul><li>Provenance computations are reflected by the use of four categories of RDFS and OWL ontology c...
Conclusion <ul><li>Findings </li></ul><ul><ul><li>Although provenance primitives can be grouped by theme, they may not be ...
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Reflections on Provenance Ontology Encodings

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Reflections on Provenance Ontology Encodings

  1. 1. Reflections on Provenance Ontology Encodings Li Ding 1 , Jie Bao 1 , James Michaelis 1 , Jun Zhao 2 , Deborah L. McGuinness 1 1 Tetherless World Constellation, RPI 2 Image Bioinformatics Research Group, Oxford
  2. 2. From Provenance Vocabulary to Provenance Ontology <ul><li>Provenance Vocabulary </li></ul><ul><ul><li>Metadata Only </li></ul></ul><ul><ul><li>e.g. PREMIS, ICS-SRC,DDMS </li></ul></ul><ul><li>Provenance Ontology </li></ul><ul><ul><li>Metadata + Inference </li></ul></ul><ul><ul><li>e.g. OPM,PML, DCTerms </li></ul></ul><ul><li>Motivation : provide guidance to understand, align and evolve existing provenance ontologies </li></ul>Source: PREMIS Data Dictionary, version 2.0 Source: http://twiki.ipaw.info/bin/view/Challenge/OPM1-01Review-Inferences
  3. 3. Semantic Web Provenance Ontologies <ul><li>Selection Criteria </li></ul><ul><ul><li>Declarative: encoded in OWL and RDFS </li></ul></ul><ul><ul><li>In-Use: applied by communities </li></ul></ul><ul><li>Selected Semantic Web Provenance Ontologies </li></ul><ul><ul><li>Open Provenance Model (OPM - Moreau et al. 2009) </li></ul></ul><ul><ul><li>Proof Markup Language (PML2 - McGuinness et al. 2007) </li></ul></ul><ul><ul><li>Dublin Core Terms (DCTerms - 2008) </li></ul></ul><ul><ul><li>Provenance Vocabulary (PRV - Hartig and Zhao 2009) </li></ul></ul><ul><ul><li>Provenir (Sahoo et al. 2008) importing OBO-RO </li></ul></ul><ul><li>Related Semantic Web ontologies: FOAF, WGS84, OWL-Time, Web of Trust, … </li></ul>
  4. 4. Examples: OPM, PML and DCTerms Source: http://dublincore.org/documents/2001/11/30/dcq-rdf-xml/ Source: OPM specification v1.1 Source: PML2 specification <ul><li>provenance concept correlation </li></ul><ul><li>provenance relation classification </li></ul><ul><li>complex provenance structure </li></ul>
  5. 5. Basic Statistics <ul><li>Small ontology </li></ul><ul><li>Within OWL/OWL2 DL expressivity </li></ul><ul><li>Not tractable (none fits in OWL2 profiles) </li></ul>* DL Expressivity is computed before importing external ontologies AL R+ HI ALCH RI(D) ALH(D) ALHF(D) ALCHIF(D) ALC F(D) DL Expressivity OWL Lite OWL DL OWL 2 DL RDFS OWL DL OWL DL OWL DL OWL Species 24 2 17 55 21 47 26 # of properties 0 8 14 22 8 30 20 # of classes 268 136 304 857 207 505 309 # of triples ro provenir prv dcterms pmlj pmlp opm
  6. 6. Semantic Analysis <ul><li>What can be learned from these OWL encodings of provenance ontology? </li></ul><ul><li>Concept Coverage (Vocabulary) </li></ul><ul><ul><li>What primitive provenance concepts should be supported? </li></ul></ul><ul><ul><li>What are the differences between the primitive concepts? </li></ul></ul><ul><li>Concept modeling (Computation) </li></ul><ul><ul><li>What kinds of provenance computation is captured? </li></ul></ul><ul><ul><li>How are computational provenance semantics modeled? </li></ul></ul>
  7. 7. Empirically Identified Themes (5W+H) <ul><li>We empirically identify themes to group provenance primitives </li></ul><ul><li>Agents (Who) : Actionable entities that can take actions in an event. </li></ul><ul><li>Artifacts (Who) : Entities made by agents and involved in events. </li></ul><ul><li>Events (What) : Observable occurrence(s), execution of action(s) (potentially including the past). </li></ul><ul><li>Methods (How): Entities denoting the operations (or actions) used (or mentioned) in events. </li></ul><ul><li>Time (When): Temporal concepts, such as time and date when things were created (or updated), primarily used for annotating events. </li></ul><ul><li>Space (Where) :Geospatial concepts such as locations, GPS coordinates and regions. </li></ul><ul><li>(Why is left out …) </li></ul>
  8. 8. Theme Coverage Analysis <ul><li>Similarity </li></ul><ul><li>Difference </li></ul><ul><ul><li>Agents: Immutable (OPM1.1), Taxonomy (e.g PML2) </li></ul></ul><ul><ul><li>Events: complex structure (OPM1.1, PML2) vs binary relation </li></ul></ul><ul><ul><li>Method: declarative method (PML2, DC, PRV) </li></ul></ul><ul><ul><li>Time: time structure (OPM1.1, DCTerms) </li></ul></ul><ul><ul><li>Space: spatial property (DCTerms, Provenir) </li></ul></ul>Spatial_parameter Location space temporal_parameter performedAt PeriodOfTime hasCreationDateTime OTime time DataCreationGuide Policy MethodOfAccrual InferenceRule methods provenir:process, ro:derives_from Execution ProvenanceStatement source pmlp:SourceUsage, pmlj:InferenceStep WasGeneratedBy Process events Data Artifact PhysicalResource IdentifiedThing, Information Artifact artifacts Agent Actor Agent Agent Agent agents Provenir (+OBO-RO) PRV core DCTerms PML 2.0 OPM 1.1
  9. 9. Computational Model <ul><li>Provenance computations are reflected by the use of four categories of RDFS and OWL ontology constructs </li></ul><ul><li>Provenance graph inference can be modeled by owl:TransitiveProperty or OWL2 property chain inference. </li></ul>X owl:propertyChainAxiom ro foaf,… reused ontology X X owl:imports Concept Reuse X X X Cardinality Restriction X X X X owl:allValuesFrom X X X X X X rdfs:domain /rdfs:range Constraints X owl:TransitiveProperty X X X owl:inverseOf Inference on relations X owl:equivalentClassOf X X X owl:unionOf X X X X owl:disjointWith X X X X X X rdfs:subPropertyOf X X X X X X rdfs:subClassOf Concept Taxonomy ro provenir prv dcterms pmlj pmlp opm
  10. 10. Conclusion <ul><li>Findings </li></ul><ul><ul><li>Although provenance primitives can be grouped by theme, they may not be fully interchangeable due to their semantic difference </li></ul></ul><ul><ul><li>The coverage on time and location themes could leverage existing ontologies such as OWL-Time </li></ul></ul><ul><ul><li>Not all provenance computations can be fully expressed using OWL (see Tao et al. 2010) </li></ul></ul><ul><li>Future work </li></ul><ul><ul><li>The results of this work will be contributions to W3C Incubator group </li></ul></ul>

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