An interactive system for finding complementary literatures: a stimulus to scientific discovery
Artificial Intelligence - Special issue on scientific discovery
Text mining: finding nuggets in mountains of textual data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Information discovery from complementary literatures: categorizing viruses as potential weapons
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
Text analysis and knowledge mining system
IBM Systems Journal
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Case mining from large databases
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Concept mining for indexing medical literature
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Prototypical case mining from biomedical literature for bootstrapping a case base
Applied Intelligence
The search for knowledge, contexts, and Case-Based Reasoning
Engineering Applications of Artificial Intelligence
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This article addresses the task of mining named relationships between concepts from biomedical literature for indexing purposes or for scientific discovery from medical literature. This research builds on previous work on concept mining from medical literature for indexing purposes and proposes to learn semantic relationships names between concepts learnt. Previous ConceptMiner system did learn pairs of concepts, expressing a relationship between two concepts, but did not learn relationships semantic names. Building on ConceptMiner, RelationshipMiner is interested in learning as well the relationships with their name identified from the Unified Medical Language System (UMLS) knowledge-base as a basis for creating higher-level knowledge structures, such as rules, cases, and models, in future work. Current system is focused on learning semantically typed relationships as predefined in the UMLS, for which a dictionary of synonyms and variations has been created. An evaluation is presented showing that actually this relationship mining task improves the concept mining task results by enabling a better screening of the relationships between concepts for relevant ones.