AYNEXT-tools for streamlining the evaluation of link prediction techniques

AYNEXT is an open source Python suite aimed towards researchers in the field of link prediction in Knowledge Graphs. Link prediction consists of predicting missing edges in a Knowledge Graph, which usually involves the application of different techniques to generate negative examples (false triples) to fit a model, and splitting edges into training, testing and validation sets. Setting up a correct evaluation setup or testing new negatives-generation strategies becomes more challenging as more complex strategies and considerations (e.g., removal of inverse relations) develop. AYNEXT makes it easy to configure and customize the creation of evaluation datasets and the computation of evaluation metrics and statistical significance tests for each pair of link prediction techniques. AYNEXT has been designed to be simple to use, but modular enough to enable customization of the main steps in the evaluation process. AYNEXT-DataGen covers the pre-processing, splitting, and negatives generation steps of the evaluation process, while AYNEXT-ResTest covers the metrics computing and the statistical tests. AYNEXT offers a simple to use command line interface that takes as input either a Knowledge Graph in standard formats or the results of applying existing techniques, but can be used programmatically for in-depth customization.& COPY; 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).