publication . Preprint . Article . 2014

An evaluative baseline for geo-semantic relatedness and similarity

Andrea Ballatore; Michela Bertolotto; David Wilson;
Open Access English
  • Published: 31 Jan 2014
Abstract
In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer's semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computationa...
Persistent Identifiers
Subjects
ACM Computing Classification System: InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
free text keywords: Computer Science - Computation and Language, Computer Science, Psychology, Geography, Planning and Development, Information Systems, geog, Computation, Geographic information system, business.industry, business, Semantics, Computer science, Natural language processing, computer.software_genre, computer, Semantic field, Artificial intelligence, Information integration, Semantic similarity
57 references, page 1 of 4

1. Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Pasca, M., Soroa, A. (2009). A study on similarity and relatedness using distributional and WordNet-based approaches. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 19{27), ACL.

2. Bakillah, M., Bedard, Y., Mostafavi, M., Brodeur, J. (2009). SIM-NET: A View-Based Semantic Similarity Model for Ad Hoc Networks of Geospatial Databases. Transactions in GIS, 13 (5-6), 417{447.

3. Ballatore, A., Wilson, D., Bertolotto, M. (2012). The Similarity Jury: Combining expert judgements on geographic concepts. In: Castano, S., Vassiliadis, P., Lakshmanan, L., Lee, M. (Eds) Advances in Conceptual Modeling. ER 2012 Workshops (SeCoGIS), LNCS, vol. 7518, Springer, pp. 231{240.

4. Ballatore, A., Bertolotto, M., Wilson, D. (2013). Computing the Semantic Similarity of Geographic Terms Using Volunteered Lexical De nitions. International Journal of Geographical Information Science, 27 (10), 2099{ 2118.

5. Ballatore, A., Bertolotto, M., Wilson, D. (2013). Geographic Knowledge Extraction and Semantic Similarity in OpenStreetMap. Knowledge and Information Systems, 37 (1), 61{81.

6. Ballatore, A., Wilson, D., Bertolotto, M. (2013). A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web. In: Pasi, G., Bordogna, G., Jain, L. (Eds) Quality Issues in the Management of Web Information, Intelligent Systems Reference Library, vol. 50, Springer, pp. 93{120.

7. Banerjee, M., Capozzoli, M., McSweeney, L., Sinha, D. (1999). Beyond Kappa: A review of interrater agreement measures. Canadian Journal of Statistics, 27 (1), 3{23.

8. Blei, D. M., Ng, A. Y., Jordan, M. I. (2003). Latent Dirichlet Allocation. The Journal of Machine Learning Research, 3, 993{1022.

9. Budanitsky, A., Hirst, G. (2006). Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics, 32 (1), 13{47.

10. Cimiano, P., Volker, J. (2005). Towards large-scale, open-domain and ontology-based named entity classi cation. In: Recent Advances in Natural Language Processing, RANLP 2005 (pp. 166{172), ACL.

11. Dawes, J. (2008). Do data characteristics change according to the number of scale points used? International Journal of Market Research, 50 (1), 61{78.

12. Ferrara, F., Tasso, C. (2013). Evaluating the Results of Methods for Computing Semantic Relatedness. In: Gelbukh, A. (ed.) Computational Linguistics and Intelligent Text Processing, LNCS, vol. 7816, Springer, pp. 447{458.

13. Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E. (2002). Placing Search in Context: The Concept Revisited. ACM Transactions on Information Systems, 20 (1), 116{131.

14. Finn, R. (1970). A Note on Estimating the Reliability of Categorical Data. Educational and Psychological Measurement, 30 (1), 71{76.

15. Goldstone, R., Son, J. (2005). Similarity. In: Holyoak, K., Morrison, R. (Eds) Cambridge Handbook of Thinking and Reasoning, New York: Cambridge University Press, pp. 13{36.

57 references, page 1 of 4
Abstract
In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer's semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computationa...
Persistent Identifiers
Subjects
ACM Computing Classification System: InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
free text keywords: Computer Science - Computation and Language, Computer Science, Psychology, Geography, Planning and Development, Information Systems, geog, Computation, Geographic information system, business.industry, business, Semantics, Computer science, Natural language processing, computer.software_genre, computer, Semantic field, Artificial intelligence, Information integration, Semantic similarity
57 references, page 1 of 4

1. Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Pasca, M., Soroa, A. (2009). A study on similarity and relatedness using distributional and WordNet-based approaches. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 19{27), ACL.

2. Bakillah, M., Bedard, Y., Mostafavi, M., Brodeur, J. (2009). SIM-NET: A View-Based Semantic Similarity Model for Ad Hoc Networks of Geospatial Databases. Transactions in GIS, 13 (5-6), 417{447.

3. Ballatore, A., Wilson, D., Bertolotto, M. (2012). The Similarity Jury: Combining expert judgements on geographic concepts. In: Castano, S., Vassiliadis, P., Lakshmanan, L., Lee, M. (Eds) Advances in Conceptual Modeling. ER 2012 Workshops (SeCoGIS), LNCS, vol. 7518, Springer, pp. 231{240.

4. Ballatore, A., Bertolotto, M., Wilson, D. (2013). Computing the Semantic Similarity of Geographic Terms Using Volunteered Lexical De nitions. International Journal of Geographical Information Science, 27 (10), 2099{ 2118.

5. Ballatore, A., Bertolotto, M., Wilson, D. (2013). Geographic Knowledge Extraction and Semantic Similarity in OpenStreetMap. Knowledge and Information Systems, 37 (1), 61{81.

6. Ballatore, A., Wilson, D., Bertolotto, M. (2013). A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web. In: Pasi, G., Bordogna, G., Jain, L. (Eds) Quality Issues in the Management of Web Information, Intelligent Systems Reference Library, vol. 50, Springer, pp. 93{120.

7. Banerjee, M., Capozzoli, M., McSweeney, L., Sinha, D. (1999). Beyond Kappa: A review of interrater agreement measures. Canadian Journal of Statistics, 27 (1), 3{23.

8. Blei, D. M., Ng, A. Y., Jordan, M. I. (2003). Latent Dirichlet Allocation. The Journal of Machine Learning Research, 3, 993{1022.

9. Budanitsky, A., Hirst, G. (2006). Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics, 32 (1), 13{47.

10. Cimiano, P., Volker, J. (2005). Towards large-scale, open-domain and ontology-based named entity classi cation. In: Recent Advances in Natural Language Processing, RANLP 2005 (pp. 166{172), ACL.

11. Dawes, J. (2008). Do data characteristics change according to the number of scale points used? International Journal of Market Research, 50 (1), 61{78.

12. Ferrara, F., Tasso, C. (2013). Evaluating the Results of Methods for Computing Semantic Relatedness. In: Gelbukh, A. (ed.) Computational Linguistics and Intelligent Text Processing, LNCS, vol. 7816, Springer, pp. 447{458.

13. Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E. (2002). Placing Search in Context: The Concept Revisited. ACM Transactions on Information Systems, 20 (1), 116{131.

14. Finn, R. (1970). A Note on Estimating the Reliability of Categorical Data. Educational and Psychological Measurement, 30 (1), 71{76.

15. Goldstone, R., Son, J. (2005). Similarity. In: Holyoak, K., Morrison, R. (Eds) Cambridge Handbook of Thinking and Reasoning, New York: Cambridge University Press, pp. 13{36.

57 references, page 1 of 4
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