Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Learning to Generate CGs from Domain Specific Sentences
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
CGWorld - Architecture and Features
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Proceedings of the 12th international conference on Intelligent user interfaces
Semantic annotation, indexing, and retrieval
Web Semantics: Science, Services and Agents on the World Wide Web
A natural language query interface to structured information
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
ESpotter: adaptive named entity recognition for web browsing
WM'05 Proceedings of the Third Biennial conference on Professional Knowledge Management
SemSearch: a search engine for the semantic web
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Ontology-based understanding of natural language queries using nested conceptual graphs
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
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A natural language interface is always desirable for a search system. While performance of machine translation for general texts with acceptable computational costs seems to reach a limit, narrowing down the domain to one of queries reduces the complexity and enables better translation correctness. This paper proposes a query translation method that is robust to ill-formed questions and exploits knowledge of an ontology for semantic search. It uses conceptual graphs as the target language for the translation. As a logical interlingua with smooth mapping to and from natural language, conceptual graphs simplify translation rules and can be easily converted to other formal query languages. Experiment results of the method on the TREC 2002 and TREC 2007 data sets are also presented and discussed.