Related papers: Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes

URL: http://arxiv.org/abs/2005.07654v2
Date: Thu, 28 May 2020 08:11:29 GMT
Title: Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
Authors: Asan Agibetov, Matthias Samwald
Abstract summary: We propose an open-source evaluation pipeline, which benchmarks the accuracy of neural embeddings. We define relation-centric connectivity measures that allow us to connect the link prediction capacity to the structure of the knowledge graph.
Score: 6.23228063561537
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. While algorithmic advances have strongly focused on efficient ways of learning embeddings, fewer attention has been drawn to the different ways their performance and robustness can be evaluated. In this work we propose an open-source evaluation pipeline, which benchmarks the accuracy of neural embeddings in situations where knowledge graphs may experience semantic and structural changes. We define relation-centric connectivity measures that allow us to connect the link prediction capacity to the structure of the knowledge graph. Such an evaluation pipeline is especially important to simulate the accuracy of embeddings for knowledge graphs that are expected to be frequently updated.