We introduce a novel method for relational learning with neural networks. The contributions of this paper are threefold. First, we introduce the concept of relational neural networ...
Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best des...
We enunciate the need for watermarking database relations to deter data piracy, identify the characteristics of relational data that pose unique challenges for watermarking, and de...
In this paper we present the Relational Bayesian Classifier (RBC), a modification of the Simple Bayesian Classifier (SBC) for relational data. There exist several Bayesian classif...
Haskell’s type system with multi-parameter constructor classes and functional dependencies allows static (compile-time) computations to be expressed by logic programming on the ...