Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Semantic distance in conceptual graphs
Conceptual structures
A library of generic concepts for composing knowledge bases
Proceedings of the 1st international conference on Knowledge capture
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
An Experiment in Document Retrieval Using Conceptual Graphs
ICCS '97 Proceedings of the Fifth International Conference on Conceptual Structures: Fulfilling Peirce's Dream
Conceptual Graph Matching for Semantic Search
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Using transformations to improve semantic matching
Proceedings of the 2nd international conference on Knowledge capture
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
MUC3 '91 Proceedings of the 3rd conference on Message understanding
University of Sheffield: description of the LaSIE system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Fuzzy conceptual graphs for matching images of natural scenes
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Proceedings of the 4th international conference on Knowledge capture
A unified knowledge based approach for sense disambiguationm and semantic role labeling
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
'Deep' grammatical relations for semantic interpretation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Learning by reading: a prototype system, performance baseline and lessons learned
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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An ultimate goal of AI is to build end-to-end systems that interpret natural language, reason over the resulting logical forms, and perform actions based on that reasoning. This requires systems from separate fields be brought together, but often this exposes representational gaps between them. The logical forms from a language interpreter may mirror the surface forms of utterances too closely to be usable as-is, given a reasoner's requirements for knowledge representations. What is needed is a system that can match logical forms to background knowledge flexibly to acquire a rich semantic model of the speaker's goal. In this paper, we present such a "matcher" that uses semantic transformations to overcome structural differences between the two representations. We evaluate this matcher in a MUC-like template-filling task and compare its performance to that of two similar systems.