  Knowledge Graphs (KGs) have found many applications in industry and academic
settings, which in turn, have motivated considerable research efforts towards
large-scale information extraction from a variety of sources. Despite such
efforts, it is well known that even state-of-the-art KGs suffer from
incompleteness. Link Prediction (LP), the task of predicting missing facts
among entities already a KG, is a promising and widely studied task aimed at
addressing KG incompleteness. Among the recent LP techniques, those based on KG
embeddings have achieved very promising performances in some benchmarks.
Despite the fast growing literature in the subject, insufficient attention has
been paid to the effect of the various design choices in those methods.
Moreover, the standard practice in this area is to report accuracy by
aggregating over a large number of test facts in which some entities are
over-represented; this allows LP methods to exhibit good performance by just
attending to structural properties that include such entities, while ignoring
the remaining majority of the KG. This analysis provides a comprehensive
comparison of embedding-based LP methods, extending the dimensions of analysis
beyond what is commonly available in the literature. We experimentally compare
effectiveness and efficiency of 16 state-of-the-art methods, consider a
rule-based baseline, and report detailed analysis over the most popular
benchmarks in the literature.
