Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
The constituent object parser: syntactic structure matching for information retrieval
ACM Transactions on Information Systems (TOIS)
Indexing medical reports in a multimedia environment: the RIME experimental approach
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
A model of information retrieval based on a terminological logic
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
COREL: a conceptual retrieval system
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Conceptual information retrieval
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
ICCS '93 Proceedings on Conceptual Graphs for Knowledge Representation
Using Conceptual Graphs in a Multifaceted Logical Model for Information Retrieval
DEXA '96 Proceedings of the 7th International Conference on Database and Expert Systems Applications
Robustness beyond shallowness: incremental deep parsing
Natural Language Engineering
Discovery of inference rules for question-answering
Natural Language Engineering
A content-search information retrieval process based on conceptual graphs
Knowledge and Information Systems
Fuzzy conceptual graphs for matching images of natural scenes
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Decision-making automation fuzzy decision-making in industry
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Expert Systems with Applications: An International Journal
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Most of Information Retrieval (IR) systems are still based on bag of word paradigm. This is a strong limitation if one needs high precision answers. For example, in restricted domain, like medicine, user builds short and precise query, like "Show me chest CT images with emphysema.", and expects from the system precise answers. In such a case, the use of natural language processing to model document content is the only way to improve IR precision. This paper presents a model for text IR that index documents with Fuzzy Conceptual Graphs (FCG). Building automatically a complete and relevant conceptual structure is known to be a difficult task. To overcome this problem and keeping automatic graph building, we promote the use of incomplete FCG. We show how to deal with this incompleteness by using confidence. This confidence is attached to concepts and conceptual relations. As we use FCG as index, the matching process is based on a fuzzy graph matching. Finally, our experiments show that this outperforms classical word based indexing.