publication . Article . Preprint . 2013

The semantic similarity ensemble

Andrea Ballatore; Michela Bertolotto; David Wilson;
Open Access
  • Published: 01 Dec 2013 Journal: Journal of Spatial Information Science (eissn: 1948-660X, Copyright policy)
  • Publisher: Journal of Spatial Information Science
Abstract
Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a sub...
Persistent Identifiers
Subjects
free text keywords: Similarity jury, Lexical similarity, Semantic similarity, Geo-semantics, Expert disagreement, WordNet, Computer Science - Computation and Language, Information integration, Perception, media_common.quotation_subject, media_common, Semantic similarity, Analogy, Subject-matter expert, Cognition, Computer science, Artificial intelligence, business.industry, business, Natural language processing, computer.software_genre, computer, Geographic information retrieval, lcsh:Geography (General), lcsh:G1-922
Funded by
SFI| SRC StratAG: Strategic Research Cluster In Advanced Geotechnologies
Project
  • Funder: Science Foundation Ireland (SFI)
  • Project Code: 07/SRC/I1168
  • Funding stream: SFI Strategic Research Cluster
Communities
Digital Humanities and Cultural Heritage
38 references, page 1 of 3

[1] AGIRRE, E., ALFONSECA, E., HALL, K., KRAVALOVA, J., PAS¸ CA, M., AND SOROA, A. 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 (2009), ACL, pp. 19-27. doi:10.3115/1620754.1620758.

[2] ARMSTRONG, J. Combining forecasts. In Principles of Forecasting: A Handbook for Researchers and Practitioners, M. Norwell, Ed. Kluwer Academic Publishing, New York, 2001, pp. 417-439.

[3] BALLATORE, A., BERTOLOTTO, M., AND WILSON, D. Geographic Knowledge Extraction and Semantic Similarity in OpenStreetMap. Knowledge and Information Systems (2012). in press.

[4] BALLATORE, A., BERTOLOTTO, M., AND WILSON, D. Computing the Semantic Similarity of Geographic Terms Using Volunteered Lexical Definitions. International Journal of Geographical Information Science (2013). in press.

[5] BALLATORE, A., BERTOLOTTO, M., AND WILSON, D. Grounding Linked Open Data in WordNet: The Case of the OSM Semantic Network. In Proceedings of the Web and Wireless Geographical Information Systems International Symposium (W2GIS 2013), S. Liang, X. Wang, and C. Claramunt, Eds., vol. 7820 of LNCS. Springer, 2013, pp. 1-15. doi:10.1007/978-3-642-37087-8 1.

Wireless Geographical Information Systems International Symposium (W2GIS 2012), S. Di Martino, A. Peron, and T. Tezuka, Eds., vol. 7236 of LNCS. Springer, 2012, pp. 151-166. doi:10.1007/978-3-642-29247-7 12.

[7] BALLATORE, A., WILSON, D., AND BERTOLOTTO, M. The Similarity Jury: Combining expert judgements on geographic concepts. In Advances in Conceptual Modeling. ER 2012 Workshops (SeCoGIS), S. Castano, P. Vassiliadis, L. Lakshmanan, and M. Lee, Eds., vol. 7518 of LNCS. Springer, 2012, pp. 231-240. doi:10.1007/978-3- 642-33999-8 29.

[8] BANERJEE, S., AND PEDERSEN, T. An adapted Lesk algorithm for word sense disambiguation using WordNet. In Computational Linguistics and Intelligent Text Processing (2002), vol. 2276 of LNCS, Springer, pp. 117-171. doi:10.1007/3-540- 45715-1 11.

[9] BAUER, A., EISENBEIS, R., WAGGONER, D., AND ZHA, T. Forecast evaluation with cross-sectional data: The Blue Chip Surveys. Economic Review-Federal Reserve Bank of Atlanta 88, 2 (2003), 17-32.

[10] BUDESCU, D., AND RANTILLA, A. Confidence in aggregation of expert opinions. Acta Psychologica 104, 3 (2000), 371-398. doi:10.1016/S0001-6918(00)00037-8.

[11] CLEMEN, R., AND WINKLER, R. Combining probability distributions from experts in risk analysis. Risk Analysis 19, 2 (1999), 187-203. doi:10.1111/j.1539- 6924.1999.tb00399.x.

[12] COOKE, R., AND GOOSSENS, L. Expert judgement elicitation for risk assessments of critical infrastructures. Journal of Risk Research 7, 6 (2004), 643-656. doi:10.1080/1366987042000192237.

[13] FELLBAUM, C. WordNet. In Theory and Applications of Ontology: Computer Applications, R. Poli, M. Healy, and A. Kameas, Eds. Springer, 2010, pp. 231-243. doi:10.1007/978-90-481-8847-5 10.

[14] FERRARA, F., AND TASSO, C. Evaluating the Results of Methods for Computing Semantic Relatedness. In Computational Linguistics and Intelligent Text Processing, A. Gelbukh, Ed., vol. 7816 of LNCS. Springer, 2013, pp. 447-458. doi:10.1007/978- 3-642-37247-6 36.

[15] HIRST, G., AND ST-ONGE, D. Lexical chains as representations of context for the detection and correction of malapropisms. In WordNet: An electronic lexical database, C. Fellbaum, Ed. MIT Press, Cambridge, MA, 1998, pp. 305-332.

38 references, page 1 of 3
Abstract
Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a sub...
Persistent Identifiers
Subjects
free text keywords: Similarity jury, Lexical similarity, Semantic similarity, Geo-semantics, Expert disagreement, WordNet, Computer Science - Computation and Language, Information integration, Perception, media_common.quotation_subject, media_common, Semantic similarity, Analogy, Subject-matter expert, Cognition, Computer science, Artificial intelligence, business.industry, business, Natural language processing, computer.software_genre, computer, Geographic information retrieval, lcsh:Geography (General), lcsh:G1-922
Funded by
SFI| SRC StratAG: Strategic Research Cluster In Advanced Geotechnologies
Project
  • Funder: Science Foundation Ireland (SFI)
  • Project Code: 07/SRC/I1168
  • Funding stream: SFI Strategic Research Cluster
Communities
Digital Humanities and Cultural Heritage
38 references, page 1 of 3

[1] AGIRRE, E., ALFONSECA, E., HALL, K., KRAVALOVA, J., PAS¸ CA, M., AND SOROA, A. 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 (2009), ACL, pp. 19-27. doi:10.3115/1620754.1620758.

[2] ARMSTRONG, J. Combining forecasts. In Principles of Forecasting: A Handbook for Researchers and Practitioners, M. Norwell, Ed. Kluwer Academic Publishing, New York, 2001, pp. 417-439.

[3] BALLATORE, A., BERTOLOTTO, M., AND WILSON, D. Geographic Knowledge Extraction and Semantic Similarity in OpenStreetMap. Knowledge and Information Systems (2012). in press.

[4] BALLATORE, A., BERTOLOTTO, M., AND WILSON, D. Computing the Semantic Similarity of Geographic Terms Using Volunteered Lexical Definitions. International Journal of Geographical Information Science (2013). in press.

[5] BALLATORE, A., BERTOLOTTO, M., AND WILSON, D. Grounding Linked Open Data in WordNet: The Case of the OSM Semantic Network. In Proceedings of the Web and Wireless Geographical Information Systems International Symposium (W2GIS 2013), S. Liang, X. Wang, and C. Claramunt, Eds., vol. 7820 of LNCS. Springer, 2013, pp. 1-15. doi:10.1007/978-3-642-37087-8 1.

Wireless Geographical Information Systems International Symposium (W2GIS 2012), S. Di Martino, A. Peron, and T. Tezuka, Eds., vol. 7236 of LNCS. Springer, 2012, pp. 151-166. doi:10.1007/978-3-642-29247-7 12.

[7] BALLATORE, A., WILSON, D., AND BERTOLOTTO, M. The Similarity Jury: Combining expert judgements on geographic concepts. In Advances in Conceptual Modeling. ER 2012 Workshops (SeCoGIS), S. Castano, P. Vassiliadis, L. Lakshmanan, and M. Lee, Eds., vol. 7518 of LNCS. Springer, 2012, pp. 231-240. doi:10.1007/978-3- 642-33999-8 29.

[8] BANERJEE, S., AND PEDERSEN, T. An adapted Lesk algorithm for word sense disambiguation using WordNet. In Computational Linguistics and Intelligent Text Processing (2002), vol. 2276 of LNCS, Springer, pp. 117-171. doi:10.1007/3-540- 45715-1 11.

[9] BAUER, A., EISENBEIS, R., WAGGONER, D., AND ZHA, T. Forecast evaluation with cross-sectional data: The Blue Chip Surveys. Economic Review-Federal Reserve Bank of Atlanta 88, 2 (2003), 17-32.

[10] BUDESCU, D., AND RANTILLA, A. Confidence in aggregation of expert opinions. Acta Psychologica 104, 3 (2000), 371-398. doi:10.1016/S0001-6918(00)00037-8.

[11] CLEMEN, R., AND WINKLER, R. Combining probability distributions from experts in risk analysis. Risk Analysis 19, 2 (1999), 187-203. doi:10.1111/j.1539- 6924.1999.tb00399.x.

[12] COOKE, R., AND GOOSSENS, L. Expert judgement elicitation for risk assessments of critical infrastructures. Journal of Risk Research 7, 6 (2004), 643-656. doi:10.1080/1366987042000192237.

[13] FELLBAUM, C. WordNet. In Theory and Applications of Ontology: Computer Applications, R. Poli, M. Healy, and A. Kameas, Eds. Springer, 2010, pp. 231-243. doi:10.1007/978-90-481-8847-5 10.

[14] FERRARA, F., AND TASSO, C. Evaluating the Results of Methods for Computing Semantic Relatedness. In Computational Linguistics and Intelligent Text Processing, A. Gelbukh, Ed., vol. 7816 of LNCS. Springer, 2013, pp. 447-458. doi:10.1007/978- 3-642-37247-6 36.

[15] HIRST, G., AND ST-ONGE, D. Lexical chains as representations of context for the detection and correction of malapropisms. In WordNet: An electronic lexical database, C. Fellbaum, Ed. MIT Press, Cambridge, MA, 1998, pp. 305-332.

38 references, page 1 of 3
Any information missing or wrong?Report an Issue