Volume 3, Issue 3, July 2013, Pages 781–793
K. Saruladha1 and E. Thirumagal2
1 Pondicherry Engineering College, Department of Computer Science and Engineering, Puducherry, India
2 Pondicherry Engineering College, Department of Computer Science and Engineering, Puducherry, India
Original language: English
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current researchers of search engines focus more on semantic based information retrieval as syntax based retrieval yield less precision. Retrieving relevant information from diverse heterogeneous web resources remains as a challenge. Distance based measures play a major role in the information retrieval systems. This work focuses on retrieving relevant concepts using geospatial datasources to aid geospatial applications in predicting floods, locating underground pipes and cables and testing the quality of water. Geospatial data characterizes geographical features of the real world using spatial extent and location. This paper proposes HDSM algorithm for geospatial information retrieval which adapts the existing distance based measures viz., Manhattan distance, Euclidean distance, Vector cosine and bray Curtis for the geospatial domain to identify related concepts to the geo-spatial query concept. All these four proposed hybrid distance based measures combine the advantages of geometric and network semantic similarity models. The meaning of the geospatial concepts are captured from the expressive knowledge of the geospatial concept properties and geospatial relations. These proposed four Hybrid distance based measures have been tested using Ordnance Survey Master Map data source and ordnance survey ontology for varying semantic similarity thresholds. The experimental results are reported in this paper. The Hybrid Manhattan distance based measure has yielded the precision of 95%.
Author Keywords: Hybrid model, Geo-spatial relations, Properties, Semantic similarity, Conceptualisation, Distance Measures.
K. Saruladha1 and E. Thirumagal2
1 Pondicherry Engineering College, Department of Computer Science and Engineering, Puducherry, India
2 Pondicherry Engineering College, Department of Computer Science and Engineering, Puducherry, India
Original language: English
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Current researchers of search engines focus more on semantic based information retrieval as syntax based retrieval yield less precision. Retrieving relevant information from diverse heterogeneous web resources remains as a challenge. Distance based measures play a major role in the information retrieval systems. This work focuses on retrieving relevant concepts using geospatial datasources to aid geospatial applications in predicting floods, locating underground pipes and cables and testing the quality of water. Geospatial data characterizes geographical features of the real world using spatial extent and location. This paper proposes HDSM algorithm for geospatial information retrieval which adapts the existing distance based measures viz., Manhattan distance, Euclidean distance, Vector cosine and bray Curtis for the geospatial domain to identify related concepts to the geo-spatial query concept. All these four proposed hybrid distance based measures combine the advantages of geometric and network semantic similarity models. The meaning of the geospatial concepts are captured from the expressive knowledge of the geospatial concept properties and geospatial relations. These proposed four Hybrid distance based measures have been tested using Ordnance Survey Master Map data source and ordnance survey ontology for varying semantic similarity thresholds. The experimental results are reported in this paper. The Hybrid Manhattan distance based measure has yielded the precision of 95%.
Author Keywords: Hybrid model, Geo-spatial relations, Properties, Semantic similarity, Conceptualisation, Distance Measures.
How to Cite this Article
K. Saruladha and E. Thirumagal, “Hybrid Distance Based Measures for Geospatial Domain,” International Journal of Innovation and Applied Studies, vol. 3, no. 3, pp. 781–793, July 2013.
