The semantic similarity ensemble
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 intr...
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doaj-2c84282b5e0349489c69729ed704b5ff2020-11-25T00:25:00ZengUniversity of MaineJournal of Spatial Information Science1948-660X2013-12-0120137274410.5311/JOSIS.2013.7.12885The semantic similarity ensembleAndrea Ballatore0Michela Bertolotto1David C. Wilson2School of Computer Science and Informatics, University College DublinSchool of Computer Science and Informatics, University College DublinDepartment of Software and Information Systems, University of North CarolinaComputational 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 subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE) as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach.http://josis.org/index.php/josis/article/view/128Similarity juryLexical similaritySemantic similarityGeo-semanticsExpert disagreementWordNet |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andrea Ballatore Michela Bertolotto David C. Wilson |
spellingShingle |
Andrea Ballatore Michela Bertolotto David C. Wilson The semantic similarity ensemble Journal of Spatial Information Science Similarity jury Lexical similarity Semantic similarity Geo-semantics Expert disagreement WordNet |
author_facet |
Andrea Ballatore Michela Bertolotto David C. Wilson |
author_sort |
Andrea Ballatore |
title |
The semantic similarity ensemble |
title_short |
The semantic similarity ensemble |
title_full |
The semantic similarity ensemble |
title_fullStr |
The semantic similarity ensemble |
title_full_unstemmed |
The semantic similarity ensemble |
title_sort |
semantic similarity ensemble |
publisher |
University of Maine |
series |
Journal of Spatial Information Science |
issn |
1948-660X |
publishDate |
2013-12-01 |
description |
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 subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE) as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach. |
topic |
Similarity jury Lexical similarity Semantic similarity Geo-semantics Expert disagreement WordNet |
url |
http://josis.org/index.php/josis/article/view/128 |
work_keys_str_mv |
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