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
SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Querying temporal databases using controlled natural language
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Proceedings of the 12th international conference on Intelligent user interfaces
A Robust Ontology-Based Method for Translating Natural Language Queries to Conceptual Graphs
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
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
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In a question answering system, users always prefer entering queries in natural language and not being constrained by a rigorous grammar. This paper proposes a syntax-free method for natural language query understanding that is robust to ill-formed questions. Nested conceptual graphs are defined as a formal target language to represent not only simple queries, but also connective, superlative, and counting queries. The method exploits knowledge of an ontology to recognize entities and determine their relations in a query. With smooth mapping to and from natural language, conceptual graphs simplify conversion rules from natural language queries and can be easily converted to other formal query languages. Experimental results of the method on the QA track datasets of TREC 2002 and TREC 2007 are presented and discussed.