Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Disambiguating prepositional phrase attachments by using on-line dictionary definitions
Computational Linguistics - Special issue of the lexicon
Analysing the dictionary definitions
Computational lexicography for natural language processing
Information retrieval
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Exploiting the Induced Order on Type-Labeled Graphs for Fast Knowledge Retrieval
ICCS '94 Proceedings of the Second International Conference on Conceptual Structures: Current Practices
ICCS '96 Proceedings of the 4th International Conference on Conceptual Structures: Knowledge Representation as Interlingua
Modeling the Semantics of Geographic Categories through Conceptual Integration
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
The analysis of noun sequences using semantic information extracted from on-line dictionaries
The analysis of noun sequences using semantic information extracted from on-line dictionaries
Structural patterns vs. string patterns for extracting semantic information from dictionaries
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Enhanced geographically typed semantic schema matching
Web Semantics: Science, Services and Agents on the World Wide Web
A conceptual framework for geographic knowledge engineering
Journal of Visual Languages and Computing
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Conceptual Graphs are a very powerful knowledge and meaning representation formalism grounded on deep philosophical, linguistic and object oriented principles [1], [2]. Concerning geographic knowledge representation and matching, the study and analysis of geographic concept definitions plays an important role in deriving systematic knowledge about concepts and comparing geographic categories in order to identify similarities and heterogeneities [4]. Based on the proposed algorithm for the representation of geographic knowledge using conceptual graphs, we also present a method that takes into consideration the special structure of conceptual graphs and produces an output that shows how much similar two geographic concepts are and hence which concept is semantically closer to another. For producing the conceptual graph representation of any geographic concept definition we follow two steps, tagging and parsing, while for measuring the similarity between two geographic ontologies we apply proper modifications to the Dice coefficient that is mainly used for comparing binary structures.