While there are a plethora of methods for link prediction in knowledge
graphs, state-of-the-art approaches are often black box, obfuscating model
reasoning and thereby limiting the ability of users to make informed decisions
about model predictions. Recently, methods have emerged to generate prediction
explanations for Knowledge Graph Embedding models, a widely-used class of
methods for link prediction. The question then becomes, how well do these
explanation systems work? To date this has generally been addressed
anecdotally, or through time-consuming user research. In this work, we present
an in-depth exploration of a simple link prediction explanation method we call
LinkLogic, that surfaces and ranks explanatory information used for the
prediction. Importantly, we construct the first-ever link prediction
explanation benchmark, based on family structures present in the FB13 dataset.
We demonstrate the use of this benchmark as a rich evaluation sandbox, probing
LinkLogic quantitatively and qualitatively to assess the fidelity, selectivity
and relevance of the generated explanations. We hope our work paves the way for
more holistic and empirical assessment of knowledge graph prediction
explanation methods in the future.

该研究提出了一种名为 LinkLogic 的简单连接预测解释方法，并构建了第一个基于家族结构的连接预测解释基准，以定量和定性评估生成的解释的忠实度、选择性和相关性。