Cheating to achieve Formal Concept analysis over a large formal context

Victor Codocedo, Carla Taramasco, Hernán Astudillo

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

16 Citas (Scopus)

Resumen

Researchers are facing one of the main problems of the Information Era. As more articles are made electronically available, it gets harder to follow trends in the different domains of research. Cheap, coherent and fast to construct knowledge models of research domains will be much required when information becomes unmanageable. While Formal Concept Analysis (FCA) has been widely used on several areas to construct knowledge artifacts for this purpose [17] (Ontology development, Information Retrieval, Software Refactoring, Knowledge Discovery), the large amount of documents and terminology used on research domains makes it not a very good option (because of the high computational cost and humanly-unprocessable output). In this article we propose a novel heuristic to create a taxonomy from a large term-document dataset using Latent Semantic Analysis and Formal Concept Analysis. We provide and discuss its implementation on a real dataset from the Software Architecture community obtained from the ISI Web of Knowledge (4400 documents).

Idioma originalInglés
Título de la publicación alojadaCLA 2011 - Proceedings of the 8th International Conference on Concept Lattices and Their Applications
EditoresVilem Vychodil, Amedeo Napoli
EditorialCEUR-WS
Páginas349-362
Número de páginas14
ISBN (versión digital)9782905267788
EstadoPublicada - 2011
Publicado de forma externa
Evento8th International Conference on Concept Lattices and Applications, CLA 2011 - Nancy, Francia
Duración: 17 oct. 201120 oct. 2011

Serie de la publicación

NombreCEUR Workshop Proceedings
Volumen959
ISSN (versión impresa)1613-0073

Conferencia

Conferencia8th International Conference on Concept Lattices and Applications, CLA 2011
País/TerritorioFrancia
CiudadNancy
Período17/10/1120/10/11

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