Details
| Original language | English |
|---|---|
| Pages (from-to) | 1-24 |
| Number of pages | 24 |
| Journal | International Journal of Geographical Information Science |
| Volume | 14 |
| Issue number | 1 |
| Publication status | Published - 1 Jan 2000 |
| Externally published | Yes |
Abstract
In order to handle the ever increasing amount of digital data efficiently, methods and techniques for automation have to be available. A key issue is the use of knowledge-based systems to interpret the data and operate on it in an automatic way. Interpretation requires knowledge about the underlying concepts in the data. Providing the necessary knowledge, however, is a bottleneck in automation. In this paper, an approach to semi-automatic knowledge acquisition is described. The knowledge is derived from given example data using supervised machine learning. An application of the approach in the domain of the interpretation of unstructured spatial data is given, and possible further applications are outlined.
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
- Social Sciences(all)
- Library and Information Sciences
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In: International Journal of Geographical Information Science, Vol. 14, No. 1, 01.01.2000, p. 1-24.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Knowledge acquisition for the automatic interpretation of spatial data
AU - Sester, Monika
PY - 2000/1/1
Y1 - 2000/1/1
N2 - In order to handle the ever increasing amount of digital data efficiently, methods and techniques for automation have to be available. A key issue is the use of knowledge-based systems to interpret the data and operate on it in an automatic way. Interpretation requires knowledge about the underlying concepts in the data. Providing the necessary knowledge, however, is a bottleneck in automation. In this paper, an approach to semi-automatic knowledge acquisition is described. The knowledge is derived from given example data using supervised machine learning. An application of the approach in the domain of the interpretation of unstructured spatial data is given, and possible further applications are outlined.
AB - In order to handle the ever increasing amount of digital data efficiently, methods and techniques for automation have to be available. A key issue is the use of knowledge-based systems to interpret the data and operate on it in an automatic way. Interpretation requires knowledge about the underlying concepts in the data. Providing the necessary knowledge, however, is a bottleneck in automation. In this paper, an approach to semi-automatic knowledge acquisition is described. The knowledge is derived from given example data using supervised machine learning. An application of the approach in the domain of the interpretation of unstructured spatial data is given, and possible further applications are outlined.
UR - http://www.scopus.com/inward/record.url?scp=0034017802&partnerID=8YFLogxK
U2 - 10.1080/136588100240930
DO - 10.1080/136588100240930
M3 - Article
AN - SCOPUS:0034017802
VL - 14
SP - 1
EP - 24
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
SN - 1365-8816
IS - 1
ER -