Title: Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs,
Abstract: We address the problem of learning vector representations for entities and
relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This
problem has received significant attention in the past few years and multiple
methods have been proposed. Most of the existing methods in the literature use
a predefined characteristic scoring function for evaluating the correctness of
KG triples. These scoring functions distinguish correct triples (high score)
from incorrect ones (low score). However, their performance vary across
different datasets. In this work, we demonstrate that a simple neural network
based score function can consistently achieve near start-of-the-art performance
on multiple datasets. We also quantitatively demonstrate biases in standard
benchmark datasets, and highlight the need to perform evaluation spanning
various datasets.