Complexity analysis and variational inference for interpretation-based probabilistic description logics

Fabio Gagliardi Cozman, Rodrigo Bellizia Polastro

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

20 Citas (Scopus)

Resumen

This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies. Inference is shown to be PEXP-complete, and variational methods are designed so as to exploit logical inference whenever possible.
Idioma originalInglés estadounidense
Páginas117-125
Número de páginas9
EstadoPublicada - 1 dic 2009
Publicado de forma externa
EventoProceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 -
Duración: 1 dic 2009 → …

Conferencia

ConferenciaProceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
Período1/12/09 → …

Citar esto

Cozman, F. G., & Polastro, R. B. (2009). Complexity analysis and variational inference for interpretation-based probabilistic description logics. 117-125. Papel presentado en Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009, .