New Perspectives for Fuzzy Datalog (Extended Abstract)

Abstract

Fuzzy logic has a long history as a tool for combining logical reasoning with the different types of uncertainty that are encountered in real-world settings by interpreting degrees of certainty as degrees of truth. This naturally motivates the study of Datalog with fuzzy logic semantics as a reasoning formalism for large databases and KGs with uncertainty. Many variants of fuzzy logic programming have been proposed in the literature [2 , 3 , 4 , 5 , 6 , 7 ], with the most active research focusing on complex multi-adjoint settings [8 , 7 ], or Prolog-derived semantics based on fuzzy similarity of constants and fuzzy unification procedures [ 5 ]. Alternative frameworks for reasoning with uncertainty like Markov Logic Networks [9 ] and Probabilistic Soft Logic [10] require extensive grounding before inference that can quickly become prohibitive when reasoning over large amounts of data. Our proposed language 𝑡-Datalog aims to be a simpler alternative focused on effective reasoning in large databases and KGs with uncertainty and aims to be the fuzzy analogue of standard Datalog. In this paper, we present the 𝑡-Datalog formalism and report on ongoing research. In particular, we present a simple and efficient fixpoint procedure for computing minimal fuzzy models for 𝑡-Datalog. Furthermore, we show how Datalog with fuzzy semantics relates to the recently proposed Datalog∘ formalism [11]