1 semantic provenance and integration peter fox and deborah l. mcguinness joint work with stephan...
TRANSCRIPT
1
Semantic Provenance and Integration
Peter Fox and Deborah L. McGuinnessJoint work with Stephan Zednick, Patrick
West, Li Ding, Cynthia Chang, …Tetherless World Constellation
Rensselaer Polytechnic Institute
Thanks to Rob Raskin (JPL), UTEP and NASA Goddard Space Flight Center Projects funded by NSF Office of Cyberinfrastructure and NASA Advanced Information
Systems Technology
2
Provenance• Origin or source from which something
comes, history of ownership, intention for use, who generated something, what is generated for, manner of manufacture, 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
Proof Markup Language (PML)
• A new kind of linked data on the Web
• Modularized & extensible– Provenance: annotate provenance properties– Justification: encodes provenance relations– Trust: add trust annotation
• Semantic Web based
Enterprise Web
Enterprise Web
World Wide Web
D D
PMLdata
PMLdata
DD
D
PMLdata
PMLdata
…
PMLdata
D
D PMLdata
PMLdata
D
4
Selected Application Drivers
Mobile Wine Agent
GILA
Combining Proofs in
TPTP
CALO
4
Knowledge Provenance
In Virtual Observatories
4
Intelligence Analyst Tools
PML Provenance It is about provenance concepts• URI for identifying and addressing • Declarative metadata• Taxonomy
#info1 a pmlp:Information;pmlp:hasRawString “(type TonysSpecialty SHELLFISH)” ;pmlp:hasLanguage <http://inference-web.org/registry/LG/KIF.owl#KIF> ;pmlp:hasFormat <http://inference-web.org//registry/FM/PDF.owl#PDF> ;pmlp:hasPrettyString “Tonys’ Specialty is ShellFish” ;
pmlp:hasURL “http://inference-web.org/documents/tonys_fact.kif”.
Science and data
• Science is built on verifiability and reproducibility
• As more layers are inserted between the scientists and the origin data, or when the data is out of the usual realm of familiarity– Trust must be established– Sources must be verifiable and proved– Explanations must be given and connected
6
20080602 Fox VSTO et al.7
8
Example Use Cases
• What was the cloud cover and atmospheric seeing conditions during the local morning of September 19, 2008 at MLSO?
• Find all good images on September 21, 2008.
• Why are the Quicklook images from September 21, 2008, 1900UT missing?
• Why does this image look bad?
9
Explain Explain Explain
20080602 Fox VSTO et al.10
11
Search and structured query
12
Search StructuredQuery
Moving to faceted browse based on PML tags (facets), using jspace
20080602 Fox VSTO et al.13
Search
14
Visual browse
15
16
17
Tools
Implementation of PML model
• Retrospective – scraping the un-related sources– Needed to gain confidence and trust from the
users– PML generated after the fact (can re-generate)
which is very good at the development stage
• Proactive – PML on the fly as data passes through the pipeline– Preferred but only when model is mature
18
Live demo
19
Further Information
• [email protected], [email protected]
• http://inference-web.org/
• http://tw.rpi.edu/portal/SPCDIS
• http://tw.rpi.edu/portal/MDSA
20