Semantic interpretation and the resolution of ambiguity
Semantic interpretation and the resolution of ambiguity
Artificial Intelligence
Artificial Intelligence - Special volume on natural language processing
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Statistical Language Learning
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
A Formal Ontology of Properties
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Computational Linguistics
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
A pragmatic treatment of quantification in natural language
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Mental States of Autonomous Agents in Competitive and Cooperative Settings
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
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It is by now widely accepted that a number of tasks in natural language understanding (NLU) require the storage of and reasoning with a vast amount of background (commonsense) knowledge. While several efforts have been made to build such ontologies, a consensus on a scientific methodology for ontological design is yet to emerge. In this paper we suggest an approach to building a commonsense ontology for language understanding using language itself as a design guide. The idea is rooted in Frege's conception of compositional semantics and is related to the idea of type inferences in strongly-typed, polymorphic programming languages. The method proposed seems to (i) resolve the problem of multiple inheritance; (ii) suggest an explanation for polysemy and metaphor; and (iii) provide a step towards establishing a systematic approach to ontological design.