The semantic similarity ensemble

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ID: 74992
2013
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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 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.
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ballatore2013thejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ballatore, Andrea;Bertolotto, Michela;Wilson, David C.;
Journal journal of spatial information science
Year 2013
DOI
DOI not found
URL
http://josis.org/index.php/josis/article/view/128
Keywords
Information technology Electrical engineering. Electronics. Nuclear engineering Science geography (general) psychology communication. mass media computer engineering. computer hardware electronic computers. computer science mathematical geography. cartography

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