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. This chapter focuses on defining the long-tail concept in the context of scientific resources, identifying the role of the SW in increasing the interoperability of long-tail resources, analyzing the reasons for their semantic heterogeneity, and introducing the design and architecture of a Geosemantic framework for addressing the semantic heterogeneity challenges associated with the interoperability of these long-tail resources
| Original language | English (US) |
|---|---|
| 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 |
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ASJC Scopus subject areas
- Computer Science(all)
- Earth and Planetary Sciences(all)
Cite this
Semantic interoperability of long-tail geoscience resources over the web. / Elag, Mostafa M.; Kumar, Praveen; Marini, Luigi; Peckham, Scott D.; Liu, Rui.
Large-Scale Machine Learning in the Earth Sciences. CRC Press, 2017. p. 175-200.Research output: Chapter in Book/Report/Conference proceeding › Chapter
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TY - CHAP
T1 - Semantic interoperability of long-tail geoscience resources over the web
AU - Elag, Mostafa M.
AU - Kumar, Praveen
AU - Marini, Luigi
AU - Peckham, Scott D.
AU - Liu, Rui
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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. This chapter focuses on defining the long-tail concept in the context of scientific resources, identifying the role of the SW in increasing the interoperability of long-tail resources, analyzing the reasons for their semantic heterogeneity, and introducing the design and architecture of a Geosemantic framework for addressing the semantic heterogeneity challenges associated with the interoperability of these long-tail resources
AB - 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. This chapter focuses on defining the long-tail concept in the context of scientific resources, identifying the role of the SW in increasing the interoperability of long-tail resources, analyzing the reasons for their semantic heterogeneity, and introducing the design and architecture of a Geosemantic framework for addressing the semantic heterogeneity challenges associated with the interoperability of these long-tail resources
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UR - http://www.scopus.com/inward/citedby.url?scp=85051805866&partnerID=8YFLogxK
U2 - 10.4324/9781315371740
DO - 10.4324/9781315371740
M3 - Chapter
AN - SCOPUS:85051805866
SN - 9781498703871
SP - 175
EP - 200
BT - Large-Scale Machine Learning in the Earth Sciences
PB - CRC Press
ER -