Fuzzy description logics (FDLs) have been introduced to represent concepts for which membership cannot be determined in a precise way, i.e., where instead of providing a strict border between being a member and not being a member, it is more appropriate to model a gradual change from membership to nonmembership. First approaches for reasoning in FDLs where based either on a reduction to reasoning in classical description logics (DLs) or on adaptations of reasoning approaches for DLs to the fuzzy case. However, it turned out that these approaches in general do not work if expressive terminological axioms, called general concept inclusions (GCIs), are available in the FDL. The goal of this project was a comprehensive study of the border between decidability and undecidability for FDLs with GCIs, as well as determining the exact complexity of the decidable logics. As a result, we have provided an almost complete classification of the decidability and complexity of FDLs with GCIs.

Baader, F., Borgwardt, S., Peñaloza, R. (2017). Decidability and Complexity of Fuzzy Description Logics. KI - KÜNSTLICHE INTELLIGENZ, 31(1), 85-90 [10.1007/s13218-016-0459-3].

Decidability and Complexity of Fuzzy Description Logics

Peñaloza, R
2017

Abstract

Fuzzy description logics (FDLs) have been introduced to represent concepts for which membership cannot be determined in a precise way, i.e., where instead of providing a strict border between being a member and not being a member, it is more appropriate to model a gradual change from membership to nonmembership. First approaches for reasoning in FDLs where based either on a reduction to reasoning in classical description logics (DLs) or on adaptations of reasoning approaches for DLs to the fuzzy case. However, it turned out that these approaches in general do not work if expressive terminological axioms, called general concept inclusions (GCIs), are available in the FDL. The goal of this project was a comprehensive study of the border between decidability and undecidability for FDLs with GCIs, as well as determining the exact complexity of the decidable logics. As a result, we have provided an almost complete classification of the decidability and complexity of FDLs with GCIs.
Articolo in rivista - Articolo scientifico
fuzzy description logics, complexity, decidability
English
2017
31
1
85
90
open
Baader, F., Borgwardt, S., Peñaloza, R. (2017). Decidability and Complexity of Fuzzy Description Logics. KI - KÜNSTLICHE INTELLIGENZ, 31(1), 85-90 [10.1007/s13218-016-0459-3].
File in questo prodotto:
File Dimensione Formato  
BaBP-KI17.pdf

accesso aperto

Tipologia di allegato: Author’s Accepted Manuscript, AAM (Post-print)
Dimensione 242.1 kB
Formato Adobe PDF
242.1 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/236230
Citazioni
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 12
Social impact