Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Toward the semantic geospatial web
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Measuring the semantic similarity of texts
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
Algorithm, implementation and application of the SIM-DL similarity server
GeoS'07 Proceedings of the 2nd international conference on GeoSpatial semantics
Grounding linked open data in wordnet: the case of the OSM semantic network
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
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A cognitively plausible measure of semantic similarity between geographic concepts is valuable across several areas, including geographic information retrieval, data mining, and ontology alignment. Semantic similarity measures are not intrinsically right or wrong, but obtain a certain degree of cognitive plausibility in the context of a given application. A similarity measure can therefore be seen as a domain expert summoned to judge the similarity of a pair of concepts according to her subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we first define the similarity jury as a panel of experts having to reach a decision on the semantic similarity of a set of geographic concepts. Second, we have conducted an evaluation of 8 WordNet-based semantic similarity measures on a subset of OpenStreetMap geographic concepts. This empirical evidence indicates that a jury tends to perform better than individual experts, but the best expert often outperforms the jury. In some cases, the jury obtains higher cognitive plausibility than its best expert.