AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Information Retrieval
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Ontological Engineering
PR-OWL: A Framework for Probabilistic Ontologies
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
Affordance-based similarity measurement for entity types
COSIT'07 Proceedings of the 8th international conference on Spatial information theory
P-CLASSIC: a tractable probablistic description logic
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A bayesian network approach to ontology mapping
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Hybrid model for semantic similarity measurement
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, COA, and ODBASE - Volume Part II
Framework for probabilistic geospatial ontologies
International Journal of Geographical Information Science
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Bayesian Network based ontologies enable specification of partial relations between concepts as an advantage over conventional ontologies, based on description logic. In the context of geospatial ontologies such specifications facilitate encoding relations between action and entitiy concepts. This paper presents a case study of transportation ontologies based on traffic code texts of two different countries. We construct ontologies of both geospatial entities and actions using the BayesOWL approach. Thereafter we employ heuristics based on verb-noun co-occurence evidences, available from analysis of formal texts, to construct linkages between the two types of concepts. This approach enables high recall and precission for querries on concepts and enables rich inferences such as most similar and disimilar concepts. The results of our experiments are verified with human subjects testing. Such heuristics-based-probablisitic approaches to geospatial ontology specification and reasoning can be utilized for concept mapping within and across geospatial ontologies as well as to quanitfy the naming hetrogeinities in two given ontologies.