Creating, Interpreting, and Repurposing Visual Messages

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@techreport{lebo_2012_creating-interpreting,
	Address = {Winslow Building, 2nd Floor 110 8th Street Troy, NY 12180},
	Author = {Timothy Lebo and Deborah L. McGuinness},
	Institution = {Rensselaer Polytechnic Institute},
	Keywords = {accountable visualization, provenance, traceability, visual transcription},
	Month = {February},
	Number = {2782},
	Title = {Creating, Interpreting, and Repurposing Visual Messages},
	Url = {http://tw.rpi.edu/web/node/2782},
	Year = {2012}}

Abstract:

The World Wide Web is a vast, diverse, and dynamic ecosys- tem of content authored and consumed with innumerable frequency. Al- though content may have an original purpose, it can and will be repur- posed in new and unexpected ways. Research in the Semantic Web has led to the pragmatic adoption of several technologies that establish the groundwork for increased machine-to-machine interoperability. Never- theless, the human factor remains – and is paramount – in both the orig- inal and semantic webs, and so is the task of transforming mechanistic content for human consumption and comprehension. Unfortunately, tra- ditional transformation methods produce isolated artifacts incapable of inspection, elaboration, and extension. This paper presents a method- ology for creating accountable visual artifacts – visual messages that preserve associations to their source content, allow traceability to their creation, and provide guidance for proper interpretation. The system is described by following two RDF triples through the originating visual- ization process, into application-specific formats, and back to an RDF- based visual transcription. We conclude by describing future work that will further increase machine accessibility, while maintaining human ap- proachability, of visual messages on the web.

Related Projects:

Inference Web Project LogoInference 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.
DCO-DS LogoLinking Open Government Data (LOGD)
Principal Investigator: Deborah L. McGuinness and Jim Hendler
Description: The LOGD project investigates the role of Semantic Web technologies, especially Linked Data, in producing, enhancing and utilizing government data published on Data.gov and other websites.

Related Research Areas:

Data Frameworks
Lead Professor: Peter Fox
Description: None.
Concepts:
Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts:
Inference And Trust
Lead Professor: Deborah L. McGuinness
Description: Inference And Trust
Concepts: Semantic Web
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance, Semantic Web
Semantic Foundations
Lead Professor: Deborah L. McGuinness
Description: Semantic Foundations
Concepts: Semantic Web
X-informatics
Lead Professor: Peter Fox
Description: In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
Concepts: Semantic Web,