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Semantic Similarity Models for Geo-spatial Information System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 64 - Number 11 |
| Year of Publication: 2013 |
| Authors: K. Saruladha, E. Thirumagal |
10.5120/10682-5569
|
K. Saruladha, E. Thirumagal . Semantic Similarity Models for Geo-spatial Information System. International Journal of Computer Applications. 64, 11 ( February 2013), 40-47. DOI=10.5120/10682-5569
Abstract
Semantic similarity measurement is the practice of estimating the relatedness of the concept based on the likeness of their meaning or their semantic content. Today's increasing interest on the geospatial information system, leads to development of the query system which needs to provide efficient geo-spatial information retrieval. The properties and spatial relations between the geo-spatial concepts must be taken into account for retrieving geo-spatial information efficiently. This paper provides the survey on various models such as geometric model, network model, transformation model, hybrid model, etc, for discovering the semantic similarity between the geo-spatial concepts. It also focuses the drawbacks of each semantic similarity model and depicts how the hybrid model works well when compared with other models in retrieving the Geo-spatial information efficiently. The main objective of this paper is to propose Hybrid semantic similarity model using Manhattan distance and by considering contexts of the Geo-spatial concepts. The Manhattan distance method is used to estimate the semantic distance between the Geo-spatial concepts and to retrieve the Geo-spatial information efficiently. The position of the locations is identified by using the map similarity.
References
- Kay Khaing Win University of ComputerStudies,Yangon, Myanmar, "Measuring Geospatial Semantic Similarity between Geospatial Entity Classes" 0-7803-9521-2/06/$20. 00 §2006 IEEE.
- Schwering, A. (2005). Hybrid model for semantic similarity measurement. 4th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE05), Agia Napa, Cyprus, Springer, 1449-1465.
- Ahlqvist, O. (2005), "Using Semantic Similarity Metrics to Uncover Category and Land Cover Change". 1st International Conference on GeoSpatial Semantics, Mexico City, Mexico, Springer, 107-119.
- Schwering, A. and M. Raubal (2005a), "Measuring semantic similarity between geospatial conceptual regions". 1st International Conference on GeoSpatial Semantics, Mexico City, Mexico, Springer, 93-109.
- Goldstone, R. L. and A. Kersten (2003), "Concepts and categorization Comprehensive Handbook of Psychology". A. F. Healy and R. W. Proctor. New Jersey, Wiley. Vol. 4: 599-621
- Gärdenfors, P. (2004). How to make the Semantic Web more semantic. Formal Ontology in Information Systems, Proceedings of the Third International Conference (FOIS2004). L. Vieu. Amsterdam, NL, IOS Press. 114: 153-164.
- Goldstone, R. L. (1994b). Similarity, Interactive Activation, and Mapping. Journal of Experimental Psychology: Learning, Memory, and Cognition 20(1): 3-28.
- Tversky, B. and P. U. Lee (1998). How space structures language. Spatial cognition: An interdisciplinary approach to representation and processing of spatial knowledge, Springer, 157-175.
- Rodríguez, A. and M. J. Egenhofer (2003). Determining semantic similarity among entity classes from different ontologies. IEEE Transactions on Knowledge and Data Engineering 15(2): 442-456.
- Li, B. and F. T. Fonseca (2006), "TDD - A Comprehensive Model for Qualitative Spatial Similarity Assessment. " Spatial Cognition and Computation 6(1): 31-62. pre print version
- Schwering, A. and M. Raubal (2005b). Spatial relations for semantic similarity measurement. 2nd International Workshop on Conceptual Modeling for Geographic Information Systems (CoMoGIS2005), Klagenfurt, Austria. , Springer, 259-269.
- A. Vadivel, A. K. Majumdar,Shamik Sural " performance comparison of distance metrics in content-based Image retrieval applications" Computer Science and Engineering, Indian Institute of Technology, Kharagpur,
- Sanjay Chawla, Shashi Shekhar, Weili Wu, and Uygar Ozesmi "Modeling Spatial Dependencies for Mining Geospatial Data"
- Rodríguez, A. and M. J. Egenhofer (2004). Comparing geospatial entity classes: An asymmetric and context-dependent similarity measure. International Journal of Geographical Information Science 18(3): 229-256.
- Resnik, P. (1999). Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11: 95-130.
- Levenshtein, I. V. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10(8): 707-710.
- Hosseinabady, M. and Nunez-Yanez, "Run-time stochastic task mapping on a large scale network-on-chip with dynamically reconfigurable tiles", J. L. Computers & Digital Techniques, IET Volume: 6 , Issue: 1 Digital Object Identifier: 10. 1049/iet-cdt. 2010. 0097 Publication Year: 2012 , Page(s): 1 - 11 IET Journals & Magazines
- Ahmad El Sayed, Hakim Hacid, Djamel Zighed, "Estimating Semantic Distance Between Concepts for Semantic Heterogeneous Information Retrieval" University of Lyon 2 ERIC Laboratory- 5, avenue Pierre Mend`es-France 69676
- Jay J. Jiang David W. Conrath, "Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy" Department of Management Sciences MGD School of Business University of Waterloo McMaster University ,Ontario, Canada N2L 3G1Hamilton, Ontario, Canada
- Montello, D. R. , S. I. Fabrikant, M. Ruocco and R. S. Middleton (2003). Testing the first law of cognitive geography on point-display spatializations. Conference on Spatial Information Theory (COSIT03), Kartause Ittingen, Switzerland, Springer, 316-331.
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