Unsupervised Mining of Knowledge Gaps in Scientific Literature
Year: 2010
Type: conference-paper
Abstract: Literature Based Discovery (LBD) relies on the identification of gaps in the scientific literature. Most of the existing methods are supervised and rely on the use of specific large knowledge domain databases like MedLine for medical study. We present here a tractable approach based on Natural Language Processing techniques with few linguistic resources and Formal Concept Lattice exploration. Entities are automatically extracted from full text scientific papers based on their acronym forms. An unsupervised classification is build using syntax and WordNet relations. Resulting classes are clustered into multiple formal concepts and the knowledge gaps are identified in the resulting Galois Lattice. The feasibility and the relevance of the outcome is analyzed on a large corpus of fulltext journal articles dealing with nuclear energy research.
Source: HAL
Institution: Laboratoire Informatique d'Avignon
Language: French
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4386941765