Third Provenance ChallengeThird Provenance ChallengeUniversity of Texas at El University of Texas at El Paso Paso Team’s PresentationTeam’s Presentation

Team: Paulo Pinheiro da Silva, Nicholas Del Rio, Leonardo Salayandia

Presenter: James Michaelis (RPI)

http://trust.utep.edu

OverviewOverview

UTEP Approach: Process and Provenance Separation

Process: Workflow-Driven Ontologies (WDO) and Semantic Abstract Workflow (SAW)◦ PC3 WDO and SAWs

Provenance: Proof Markup Language (PML)◦ PC3 PML◦ Capturing PC3 PML

Answering PC3 QuestionsConclusions

UTEP ApproachUTEP ApproachDifferent than OPM that considers process and

provenance knowledge altogether, UTEP uses Inference Web technology that has an explicit separation between process and provenance knowledge ◦ Inference Web work on provenance was originally

developed in the context of theorem provers instead of scientific workflows

◦ Inference Web has been expanded to include support for scientific workflows

◦ Separation between process and provenance has been preserved (and is considered beneficial considering many provenance scenarios without process knowledge)

Process knowledge: Workflow-Driven Ontology (WDO) and Semantic Abstract Workflow (SAW)

Provenance knowledge: Proof Markup Language (PML)

WDOs and SAWsWDOs and SAWsWDOs are OWL-based ontologies used

to represent process-related concepts, which are classified either as Data or Methods

WDO concepts can be created or reused from other domain ontologies as needed during the specification of processes

SAWs are built using instances of the WDO concepts connected through isInputTo and isOutputOf relations (and their inverses)

WDO-It! is a graphic editor for WDOs and SAWs

PC3 Semantic Abstract PC3 Semantic Abstract WorkflowWorkflow

WDO Data instances

WDO Method

instances

PML-P Agent instances: Data comes from or goes to PML-P

Agent

Data isOutputOf Method

Data isInputTo Method

Abstraction at multiple levels of

detail

Proof Markup Language Proof Markup Language (PML)(PML)PML is an OWL-based ontology

composed of three modules:◦PML-J (justifications): used to build

information manipulation traces (or justifications) for a given response (or result)

◦PML-P (provenance): used to annotate PML-J documents with metadata about sources, methods (called inference rules), and agents

◦PML-T (trust): used to annotate PML-J with trust and belief metadata about agents and conclusions

PC3 PML EncodingPC3 PML Encoding<rdf:RDF> <NodeSet rdf:about="http://iw.utep.edu/pml/compactedDerbyDB_.owl#answer"> <hasConclusion> <pmlp:Information> <pmlp:hasURL rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI" > http://iw.cs.utep.edu/pc3/databases/J062941_LoadDB_022030949845896586 </pmlp:hasURL> <pmlp:hasFormat rdf:resource="http://iw.utep.edu/registry/FMT/derbyDB.owl#derbyDB"/> </pmlp:Information> </hasConclusion> <isConsequentOf> <InferenceStep>

<hasInferenceEngine rdf:resource="http://iw.utep.edu/registry/IE/PC3-PSLoadExecutable.owl#PC3"/> <hasInferenceRule rdf:resource="http://iw.utep.edu/registry/RUL/compactDB.owl#compactDB"/> <hasIndex rdf:datatype="http://www.w3.org/2001/XMLSchema#int" >0</hasIndex> <hasAntecedentList> <NodeSetList> <ds:first rdf:resource="http://iw.utep.edu/pml/derbyDB_3.owl#answer"/> </NodeSetList> </hasAntecedentList> </InferenceStep> </isConsequentOf> </NodeSet> </rdf:RDF>

OPM:Artifact

OPM:Process

OPM:WasGeneratedBy

OPM:WasControlledBy

PML CapturePML CaptureFrom a given SAW, WDO-It! has

two options to generate code capable of capturing provenance: ◦Generate PML wrappers

used for run-time capture of provenance

◦Generate PML data annotators used for post-execution generation of

provenance

Answering PC3 Questions :Answering PC3 Questions :What proc. steps were used?What proc. steps were used?

SPARQL can be used to query the PML provenance graph.

This example shows how a SPARQL query could use the PML graph to answer what processing steps were used to generate some artifact.

ConclusionConclusionThe full encoding of the WDO, SAWs

and PML for PC3 was done in 36 hours

UTEP’s approach relies on tools to:◦Understand and speed-up the encoding

of process knowledge (as WDOs and SAWs)

◦Use process knowledge to create PML wrappers and/or PML data annotators

◦Visualize and browse provenance◦Use provenance for explanations, trust

computation, data discovery, etc.

AcknowledgementsAcknowledgementsUTEP would like to thank James

Michaelis for his effort to understand our work and represent our team at the 3rd Provenance Challenge

UTEP would like to thank the 3rd Provenance Challenge organizers and Paul Groth in particular for creating an opportunity for our team to be represented at the event