Abstract
Scientists today have to manipulate resources such as data and models, which originate and reside in multiple autonomous and heterogeneous repositories over the Web. Several resource management systems have emerged within geoscience communities for sharing long-tail data, which are collected by individual or small research groups, and long-tail models, which are developed by scientists or small modeling communities. While these systems have increased the availability of resources within geoscience domains, deficiencies remain due to heterogeneity in the methods that are used to describe, encode, and publish information about resources over the Web. This heterogeneity limits our ability to access the right information in the right context so that it can be efficiently retrieved and understood by humans and machines-without the scientist’s mediation. A primary challenge of the Web today is the lack of semantic interoperability among the massive number of resources that already exist and are continually being generated at rapid rates. The Semantic Web (SW) holds the promise to build a Web-scale topology of linked and interoperable resources, which will allow users to search simultaneously across many different and distributed information structures.
Original language | English (US) |
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Title of host publication | Large-Scale Machine Learning in the Earth Sciences |
Publisher | CRC Press |
Pages | 175-200 |
Number of pages | 26 |
ISBN (Electronic) | 9781498703888 |
ISBN (Print) | 9781498703871 |
DOIs | |
State | Published - Jan 1 2017 |
ASJC Scopus subject areas
- General Computer Science
- General Earth and Planetary Sciences