Authors:Li Ding, James Michaelis, James McCusker, & Deborah L. McGuinness
Concepts:Provenance, eScience, Web Science, Semantic Web, Semantic Web Services, & Data Science
Abstract:
Date | Created By | Link |
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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 |
![]() | 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. |
![]() | 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 |