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Foundations VI: Provenance

Foundations VI: Provenance. Deborah McGuinness TA Weijing Chen Semantic eScience Week 9, October 31, 2011. References. 2.

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Foundations VI: Provenance

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  1. Foundations VI: Provenance Deborah McGuinness TA Weijing Chen Semantic eScience Week 9, October 31, 2011

  2. References 2 • PML -McGuinness, Ding, Pinheiro da Silva, Chang. PML 2: A Modular Explanation Interlingua. AAAI 2007 Workshop on Explanation-aware Computing, Vancouver, Can., 7/07. Stanford Tech report KSL-07-07. http://www.ksl.stanford.edu/KSL_Abstracts/KSL-07-07.html • Inference Web - McGuinness and Pinheiro da Silva. Explaining Answers from the Semantic Web: The Inference Web Approach. Web Semantics: Science, Services and Agents on the World Wide Web Special issue: International Semantic Web Conference 2003 - Edited by K.Sycara and J.Mylopoulis. Volume 1, Issue 4. Journal published Fall, 2004 http://www.ksl.stanford.edu/KSL_Abstracts/KSL-04-03.html • McGuinness, D.L.; Zeng, H.; Pinheiro da Silva, P.; Ding, L.; Narayanan, D.; Bhaowal, M. Investigations into Trust for Collaborative Information Repositories: A Wikipedia Case Study. The Workshop on the Models of Trust for the Web (MTW'06), Edinburgh, Scotland, May 22, 2006. 2006. http://www.ksl.stanford.edu/KSL_Abstracts/KSL-06-05.html • More from http://inference-web.org/wiki/Publications • Note the LOGD converter generates PML

  3. Semantic Web Methodology and Technology Development Process • Establish and improve a well-defined methodology vision for Semantic Technology based application development • Leverage controlled vocabularies, et c. Adopt Technology Approach Leverage Technology Infrastructure Science/Expert Review & Iteration Rapid Prototype Open World: Evolve, Iterate, Redesign, Redeploy Use Tools Evaluation Analysis Use Case Develop model/ ontology Small Team, mixed skills

  4. Ingest/pipelines: problem definition • Data is coming in faster, in greater volumes and outstripping our ability to perform adequate quality control • Data is being used in new ways and we frequently do not have sufficient information on what happened to the data along the processing stages to determine if it is suitable for a use we did not envision • We often fail to capture, represent and propagate manually generated information that need to go with the data flows • Each time we develop a new instrument, we develop a new data ingest procedure and collect different metadata and organize it differently. It is then hard to use with previous projects • The task of event determination and feature classification is onerous and we don't do it until after we get the data

  5. 20080602 Fox VSTO et al.

  6. Use cases • Who (person or program) added the comments to the science data file for the best vignetted, rectangular polarization brightness image from January, 26, 2005 1849:09UT taken by the ACOS Mark IV polarimeter? • What was the cloud cover and atmospheric seeing conditions during the local morning of January 26, 2005 at MLSO? • Find all good images on March 21, 2008. • Why are the quick look images from March 21, 2008, 1900UT missing? • Why does this image look bad?

  7. 20080602 Fox VSTO et al.

  8. 20080602 Fox VSTO et al.

  9. Provenance • Origin or source from which something comes, intention for use, who/what generated for, manner of manufacture, history of subsequent owners, sense of place and time of manufacture, production or discovery, documented in detail sufficient to allow reproducibility • Knowledge provenance; enrich with ontologies and ontology-aware tools

  10. Semantic Technology Foundations PML – Proof Markup Language – used for knowledge provenance interlingua Inference Web Toolkit – used to manipulate and access knowledge provenance OWL-DL ontologies (including SWEET and VSTO ontologies) PML -McGuinness, Ding, Pinheiro da Silva, Chang. PML 2: A Modular Explanation Interlingua. AAAI 2007 Workshop on Explanation-aware Computing, Vancouver, Can., 7/07. Stanford Tech report KSL-07-07. Inference Web - McGuinness and Pinheiro da Silva. Explaining Answers from the Semantic Web: The Inference Web Approach. Web Semantics: Science, Services and Agents on the World Wide Web Special issue: International Semantic Web Conference 2003 - Edited by K.Sycara and J.Mylopoulis. Volume 1, Issue 4. Journal published Fall, 2004

  11. Semantic Web based infrastructure PML is an explanation interlingua Represent knowledge provenance (who, where, when…) Represent justifications and workflow traces across system boundaries Inference Web provides a toolkit for data management and visualization Inference Web Explanation Architecture WWW Toolkit Trust computation IWTrust OWL-S/BPEL SDS Trace of web service discovery Proof Markup Language (PML) End-user friendly visualization IW Explainer/ Abstractor * Learners Learning Conclusions Expert friendly Visualization Trust KIF/N3 JTP/CWM IWBrowser Theorem prover/Rules search engine based publishing Justification SPARK-L SPARK IWSearch Trace of task execution Provenance provenance registration Text Analytics IWBase UIMA Trace of information extraction

  12. Global View and More Explanation as a graph Customizable browser options Proof style Sentence format Lens magnitude Lens width More information Provenance metadata Source PML Proof statistics Variable bindings Link to tabulator … Views of Explanation filtered focused global abstraction Explanation (in PML) discourse trust provenance

  13. Provenance View Source metadata: name, description, … Source-Usage metadata: which fragment of a source has been used when Views of Explanation filtered focused global abstraction Explanation (in PML) discourse trust provenance

  14. Trust View (preliminary) simple trust representation Provides colored (mouseable) view based on trust values Enables sharing and collaborative computation and propagation of trust values Views of Explanation filtered focused global abstraction Explanation (in PML) Trust Tab Detailed trust explanation discourse trust provenance Fragment colored by trust value

  15. Discourse View (Limited) natural language interface Mixed initiative dialogue Exemplified in CALO domain Explains task execution component powered by learned and human generated procedures Views of Explanation filtered focused global abstraction Explanation (in PML) discourse trust provenance

  16. Selected IW and PML Applications Portable proofs across reasoners: JTP (with temporal and context reasoners (Stanford); CWM (W3C), SNARK(SRI), … Explaining web service composition and discovery (SNRC) Explaining information extraction (more emphasis on provenance – KANI, UIMA) Explaining intelligence analysts’ tools (NIMD/KANI) Explaining tasks processing (SPARK / CALO) Explaining learned procedures (TAILOR, LAPDOG, / CALO) Explaining privacy policy law validation (TAMI) Explaining decision making and machine learning (GILA) Explaining trust in social collaborative networks (TrustTab) Registered knowledge provenance: IW Registrar (Explainable Knowledge Aggregation) Explaining natural science provenance – VSTO, SPCDIS, …

  17. PML1 vs. PML2 PML1 was introduced in 2002 It has been used in multiple contexts ranging from explaining theorem provers to text analytics to machine learning. It was specified as a single ontology PML2 improves PML1 by Adopting a modular design: splitting the original ontology into three pieces: provenance, justification, and trust This improves reusability, particularly for applications that only need certain explanation aspects, such as provenance or trust. Enhancing explanation vocabulary and structure Adding new concepts, e.g. information Refining explanation structure

  18. PML Provenance Ontology Scope: annotating provenance metadata Highlights Information Source Hierarchy Source Usage

  19. Referencing, Encoding and Annotating a Piece of Information Referencing a piece of information using URI Encoding the content of information Complete Quote: <hasRawString>(type TonysSpecialty SHELLFISH) </hasRawString> Obtained from URL: <hasURL>http://inference-web.org/ksl/registry/storage/documents/tonys_fact.kif</hasURL> Annotations For human consumption:<hasPrettyString>Tonys’ Specialty is ShellFish</hasPrettyString> For machine consumption Language: <hasLanguage rdf:resource="http://inference-web.org/registry/LG/KIF.owl#KIF" /> Format: <hasFormat "http://inference-web.org//registry/FM/PDF.owl#PDF" />

  20. Source Hierarchy Source is the container of information Our source hierarchy offers Many well-known sources such as Sensor (e.g. geo-science) InferenceEngine (e.g. reasoner) WebService (e.g. workflow) Finer granularity of source than just document DocumentFragment (for text analytics)

  21. Source Usage Source Usage logs the action that accesses a source at a certain dateTime to retrieve information is part of PML1 Example: Source #ST was accessed on certain date <pmlp:SourceUsage rdf:about="#usage1"> <pmlp:hasUsageDateTime>2005-10-17T10:30:00Z</pmlp:hasUsageDateTime> <pmlp:hasSource rdf:resource="#ST"/> </pmlp:SourceUsage>

  22. PML Justification Ontology Scope: annotating justification process Highlights Template for question-answer/justification Four types of justification

  23. Four Types of Justification

  24. PML Trust Ontology Scope: annotate trust and belief assertions Highlights Extensible trust representation (user may plug in their quantitative metrics using OWL class inheritance feature) Has been used to provide a trust tab filter for wikipedia – see McGuinness, Zeng, Pinheiro da Silva, Ding, Narayanan, and Bhaowal. Investigations into Trust for Collaborative Information Repositories: A Wikipedia Case Study. WWW2006 Workshop on the Models of Trust for the Web (MTW'06), Edinburgh, Scotland, May 22, 2006.

  25. 20080602 Fox VSTO et al.

  26. Quick look browse 20080602 Fox VSTO et al.

  27. Visual browse

  28. Search and structured query Structured Query Search

  29. Search 20080602 Fox VSTO et al.

  30. Provenance within SemantAqua

  31. SemantAqua System Architecture Virtuoso access

  32. Provenance • Preserves provenance in the Proof Markup Language (PML). • Data Source Level Provenance: • Thecaptured provenance data are used to support provenance-based queries. • Reasoning level provenance: • When water source been marked as polluted, user can access supporting provenance data for the explanations including the URLs of the source data, intermediate data, the converted data, and regulatory data.

  33. Visualization http://tinyurl.com/iswc-swqp

  34. Visualization http://tinyurl.com/iswc-swqp

  35. Visualization http://tinyurl.com/iswc-swqp

  36. Visualization http://tinyurl.com/iswc-swqp

  37. Visualization http://tinyurl.com/iswc-swqp

  38. Visualization • Time series Visualization: • Presents data in time series visualization for user to explore and analyze the data Violation, measured value: 2032.8 Violation, measured value: 971 Limit value: 400 http://was.tw.rpi.edu/swqp/trend/epaTrend.html?state=RI&county=1&site=http%3A%2F%2Ftw2.tw.rpi.edu%2Fzhengj3%2Fowl%2Fepa.owl%23facility-110009444869

  39. Selected Results • Provenance information encoded using semantic web technology supports transparency and trust. • SemantAqua provides detailed provenance information: • Original data, intermediate data, data source • “What if” Scenario: • User can apply a stricter regulation from another state to a local water source. • User may be interested only in certain sources and can use the interface to control queries

  40. Aim at providing at least as much provenance as SemantAqua • Questions?

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