1 Foundations VI: Provenance Deborah McGuinness and Peter Fox CSCI Week 12, November 30, 2009

References 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 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 Edited by K.Sycara and J.Mylopoulis. Volume 1, Issue 4. Journal published Fall, 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, More from 2

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. Use Case Small Team, mixed skills Analysis Adopt Technology Approach Leverage Technology Infrastructure Rapid Prototype Open World: Evolve, Iterate, Redesign, Redeploy Use Tools Science/Expert Review & Iteration Develop model/ ontology Evaluation

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

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6 Who (person or program) added the comments to the science data file for the best vignetted, rectangular polarization brightness image from January, 26, :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, Why are the quick look images from March 21, 2008, 1900UT missing? Why does this image look bad? Use cases

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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

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 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 Edited by K.Sycara and J.Mylopoulis. Volume 1, Issue 4. Journal published Fall, 2004

WWW Toolkit Proof Markup Language (PML) Learners JTP/CWM SPARK UIMA IW Explainer/ Abstractor IWBase IWBrowser IWSearch Trust Justification Provenance * KIF/N3 SPARK-L Text Analytics IWTrust provenance registration search engine based publishing Expert friendly Visualization End-user friendly visualization Trust computation OWL-S/BPEL SDS Trace of web service discovery Learning Conclusions Trace of task execution Trace of information extraction Theorem prover/Rules Inference Web Explanation Architecture 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

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 Explanation (in PML) filteredfocusedglobal abstraction discourse provenance trust

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

Trust Tab Fragment colored by trust value Detailed trust explanation 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 Explanation (in PML) filteredfocusedglobal abstraction discourse provenance trust

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 Explanation (in PML) filteredfocusedglobal abstraction discourse provenance trust

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, …

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

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

Referencing, Encoding and Annotating a Piece of Information Referencing a piece of information –using URI Encoding the content of information –Complete Quote: (type TonysSpecialty SHELLFISH) –Obtained from URL: web.org/ksl/registry/storage/documents/tonys_fact.kif Annotations –For human consumption: Tonys’ Specialty is ShellFish –For machine consumption Language: Format:

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)

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 T10:30:00Z

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

Four Types of Justification Goalconclusion without justification Assumptionconclusion assumed (using Assumption Rule) asserted by an InferenceEngine, no antecedent Direct Assertionconclusion directly asserted (using DirectAssertion rule) by an InferenceEngine, no antecedent Regularconclusion derived from antecedent conclusions

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.

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Fox VSTO et al. 27 Quick look browse

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29 Visual browse

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Search and structured query 32 Search Structured Query

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Next week Next class –Architecture and Middleware Questions? 34