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Foundations VI: Provenance
Deborah McGuinness and Peter Fox
CSCI-6962-01
Week 12, November 30, 2009
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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-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
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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.
Leverage Adopt
Rapid Technology Science/Expert
Open World: Prototype Technology Approach Review & Iteration
Evolve, Iterate, Infrastructure
Redesign,
Redeploy
Use Tools
Evaluation
Analysis
Use Case
Small Team, Develop
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mixed skills model/
ontology
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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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?
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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
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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
Inference Web Explanation Architecture
WWW Toolkit
IWTrust Trust computation
SDS OWL-S/BPEL
Trace of web service discovery
Proof Markup IW Explainer/ End-user friendly
Learners * Language (PML) Abstractor
visualization
Learning Conclusions
Expert friendly
JTP/CWM KIF/N3 Trust IWBrowser Visualization
Theorem prover/Rules
Justification search engine
SPARK SPARK-L IWSearch based publishing
Trace of task execution
Provenance provenance
UIMA Text Analytics IWBase registration
Trace of information extraction
• 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 Views of Explanation
filtered focused global
Explanation abstraction
(in PML)
discourse
trust
provenance
• 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
Provenance View Views of Explanation
• Source metadata: name, description, … filtered focused global
• Source-Usage metadata: which fragment of
a source has been used when abstraction
Explanation
(in PML)
discourse
trust
provenance
Trust View Views of Explanation
filtered focused global
Trust Tab Explanation abstraction
Detailed trust
(in PML)
explanation
discourse
trust
provenance
• (preliminary) simple
trust representation
• Provides colored
(mouseable) view
based on trust values
• Enables sharing and
collaborative
computation and
propagation of trust
Fragment values
colored by
trust value
Discourse View Views of Explanation
• (Limited) natural language interface filtered focused global
• Mixed initiative dialogue
• Exemplified in CALO domain Explanation
(in PML)
abstraction
• Explains task execution component trust
discourse
powered by learned and human provenance
generated procedures
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:
<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" />
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
• 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>
PML Justification Ontology
• Scope: annotating
justification process
• Highlights
– Template for question-
answer/justification
– Four types of justification
Four Types of Justification
Goal conclusion without justification
Assumption conclusion assumed (using
Assumption Rule) asserted by an
InferenceEngine, no antecedent
Direct Assertion conclusion directly asserted (using
DirectAssertion rule) by an
InferenceEngine, no antecedent
Regular conclusion 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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Quick look browse
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Visual browse
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Search and structured query
Search Structured
Query
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Search
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Next week
• Next class
– Architecture and Middleware
• Questions?
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