Vítor Santos Costa

Abstract
Prolog systems rely on an atom table for symbol management, which is usually implemented as a dynamically resizeable hash table. This is ideal for single threaded execution, but can become a bottleneck in a multi-threaded scenario. In this work, we replace the original atom table implementation in the YAP Prolog system with a lock-free hash-based data structure, named Lock-free Hash Tries (LFHT), in order to provide efficient and scalable symbol management. Being lock-free, the new implementation also provides better guarantees, namely, immunity to priority inversion, to deadlocks and to livelocks. Performance results show that the new lock-free LFHT implementation has better results in single threaded execution and much better scalability than the original lock based dynamically resizing hash table.

Abstract

Abstract

Abstract

Abstract
In this paper, we present two online structure learning algorithms for NeuralLog, NeuralLog+OSLR and NeuralLog+OMIL. NeuralLog is a system that compiles first-order logic programs into neural networks. Both learning algorithms are based on Online Structure Learner by Revision (OSLR). NeuralLog+OSLR is a port of OSLR to use NeuralLog as inference engine; while NeuralLog+OMIL uses the underlying mechanism from OSLR, but with a revision operator based on Meta-Interpretive Learning. We compared both systems with OSLR and RDN-Boost on link prediction in three different datasets: Cora, UMLS and UWCSE. Our experiments showed that NeuralLog+OMIL outperforms both the compared systems on three of the four target relations from the Cora dataset and in the UMLS dataset, while both NeuralLog+OSLR and NeuralLog+OMIL outperform OSLR and RDNBoost on the UWCSE, assuming a good initial theory is provided.