Ontology Dynamics in a Data Life Cycle: Challenges and Recommendations from a Geoscience Perspective

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Abstract:

Ontologies are increasingly deployed as a computer-accessible representation of key semantics in various parts of a data life cycle and, thus, ontology dynamics may pose challenges to data management and re-use. By using examples in the field of geosciences, we analyze challenges raised by ontology dynamics, such as heavy reworking of data, semantic heterogeneity among data providers and users, and error propagation in cross-discipline data discovery and re-use. We also make recommendations to address these challenges: (1) communities of practice on ontologies to re- duce inconsistency and duplicated efforts; (2) use ontologies in the procedure of data collection and make them accessible to data users; and (3) seek methods to speed up the reworking of data in a Semantic Web context.

History

DateCreated ByLink
August 17, 2014
22:44:02
Patrick WestDownload

Related Projects:

Global Change Information System: Information Model and Semantic Application Prototypes (GCIS-IMSAP)
Principal Investigator: Peter Fox
Description: The Tetherless World Constellation (TWC) at Rensselaer Polytechnic Institute (RPI) proposes to facilitate the vocabulary and ontology development within the context of the overall development of semantic prototypes for the National Climate Assessment (NCA) portals using a combination of environmental inter-agency collaborations in a use-case focused workshop setting, information modeling, and software developments and deployments. The prototypes are intended to provide search and browse options that inspire confidence that all relevant information has been found; data providers will be citable with detailed provenance generation. Expected deliverables are: information models, vocabulary and ontology services for vetted climate assessment settings, and search/ browse prototypes.

Related Research Areas:

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: eScience