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Linked provenance data: A semantic Web-based approach to interoperable workflow traces
Citation: Ding, L., Michaelis, J., McCusker, J., and McGuinness, D.L. 2011. Linked provenance data: A semantic Web-based approach to interoperable workflow traces. Future Generation Computer Systems, pages 797–805.
Published in Future Generation Computer Systems
Published in Future Generation Computer Systems
Authors:Li Ding, James Michaelis, James McCusker, & Deborah L. McGuinness
Concepts:Provenance, eScience, Web Science, Semantic Web, Semantic Web Services, & Data Science
Abstract:
The Third Provenance Challenge (PC3) offered an opportunity for provenance researchers to evaluate the interoperability of leading provenance models with special emphasis on importing and querying workflow traces generated by others. We investigated interoperability issues related to reusing Open Provenance Model (OPM)-based workflow traces. We compiled data about interoperability issues that were observed during PC3 and use that data to help describe and motivate solution paths for two outstanding interoperability issues in OPM-based provenance data reuse: (i) a provenance trace often requires both generic provenance data and domain-specific data to support future reuse (such as querying); (ii) diverse provenance traces (possibly from different sources) often require preservation and interconnection to support future aggregation and comparison. In order to address these issues and to facilitate interoperable reuse, integration, and alignment of provenance data, we propose a Semantic Web-based approach known as Linked Provenance Data, where: (i) the Web Ontology Language (OWL) can be used to support complex domain concept modeling, such as subtype taxonomy and concept alignment, and seamlessly connect domain extensions to OPM core concepts; (ii) Linked Data can enable open and transparent infrastructure for provenance data reuse.
History
Move:
| Date | Created By | Link |
|---|---|---|
| July 19, 2011 00:26:55 | James Michaelis | Download |
| July 18, 2011 16:02:54 | James Michaelis | Download |
| March 3, 2011 00:01:19 | Patrick West | Download |
Related Projects:
Move:
 | Inference Web Principal Investigator: Deborah L. McGuinness Description: The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust. Provenance - if users (humans and agents) are to use and integrate data from unknown, uncertain, or multiple sources, they need provenance metadata for evaluation Interoperability - more systems are using varied sources and multiple information manipulation engines, thus increasing interoperability requirements Explanation/Justification - if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available Trust - if some sources are more trustworthy than others, trust ratings are desired The Inference Web consists of two important components: Proof Markup Language (PML) Ontology - Semantic Web based representation for exchanging explanations including provenance information - annotating the sources of knowledge justification information - annotating the steps for deriving the conclusions or executing workflows trust information - annotating trustworthiness assertions about knowledge and sources IW Toolkit - Web-based and standalone tools that facilitate human users to browse, debug, explain, and abstract the knowledge encoded in PML. |
Related Research Areas:
Move:
 | Data Frameworks Lead Professor: Peter Fox Description: None. Concepts: eScience |
 | Knowledge Provenance Lead Professor: Deborah L. McGuinness Description: Concepts: Provenance, Semantic Web |
 | Semantic eScience Lead Professor: Peter Fox Description:
Science has fully entered a new mode of operation. E-science,
defined as a combination of science, informatics, computer
science, cyberinfrastructure and information technology is
changing the way all of these disciplines do both their
individual and collaborative work.
As semantic technologies have been gaining momentum in various
e-Science areas (for example, W3C's new interest group for
semantic web health care and life science), it is important to
offer semantic-based methodologies, tools, middleware to
facilitate scientific knowledge modeling, logical-based
hypothesis checking, semantic data integration and application
composition, integrated knowledge discovery and data analyzing
for different e-Science applications.
Partially influenced by the Artificial Intelligence community,
the Semantic Web researchers have largely focused on formal
aspects of semantic representation languages or general-purpose
semantic application development, with inadequate consideration
of requirements from specific science areas. On the other hand,
general science researchers are growing ever more dependent on
the web, but they have no coherent agenda for exploring the
emerging trends on the semantic web technologies. It urgently
requires the development of a multi-disciplinary field to foster
the growth and development of e-Science applications based on
the semantic technologies and related knowledge-based
approaches.
Concepts: eScience |
 | Web Science Lead Professor: Jim Hendler, Deborah L. McGuinness Description: Concepts: Semantic Web |
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