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1 Semantic Provenance and Integration Peter Fox and Deborah L. McGuinness Joint work with Stephan Zednick, Patrick West, Li Ding, Cynthia Chang, … Tetherless.

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Presentation on theme: "1 Semantic Provenance and Integration Peter Fox and Deborah L. McGuinness Joint work with Stephan Zednick, Patrick West, Li Ding, Cynthia Chang, … Tetherless."— Presentation transcript:

1 1 Semantic Provenance and Integration Peter Fox and Deborah L. McGuinness Joint 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 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

3 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 World Wide Web DD PML data PML data D D D PML data PML data … PML data D D PML data PML data D

4 4 Selected Application Drivers Mobile Wine Agent GILA Combining Proofs in TPTP CALO 4 Knowledge Provenance In Virtual Observatories 4 Intelligence Analyst Tools

5 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 ; pmlp:hasFormat ; pmlp:hasPrettyString “Tonys’ Specialty is ShellFish” ; pmlp:hasURL “http://inference-web.org/documents/tonys_fact.kif ”.

6 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

7 20080602 Fox VSTO et al. 7

8 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 9 Explain

10 20080602 Fox VSTO et al. 10

11 11

12 Search and structured query 12 Search Structured Query Moving to faceted browse based on PML tags (facets), using jspace

13 20080602 Fox VSTO et al. 13 Search

14 14 Visual browse

15 15

16 16

17 17 Tools

18 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

19 Live demo 19

20 Further Information pfox@cs.rpi.edu, dlm@cs.rpi.edupfox@cs.rpi.edudlm@cs.rpi.edu http://inference-web.org/ http://tw.rpi.edu/portal/SPCDIS http://tw.rpi.edu/portal/MDSA 20


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