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。

LinkLogic: 透明的知识图谱预测的新方法和基准测试

LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph  Predictions

Video analytics systems based on deep learning models are often opaque and
brittle and require explanation systems to help users debug. Current model
explanation system are very good at giving literal explanations of behavior in
terms of pixel contributions but cannot integrate information about the
physical or systems processes that might influence a prediction. This paper
introduces the idea that a simple form of causal reasoning, called a regression
discontinuity design, can be used to associate changes in multiple key
performance indicators to physical real world phenomena to give users a more
actionable set of video analytics explanations. We overview the system
architecture and describe a vision of the impact that such a system might have.

基于深度学习模型的视频分析系统常常是不透明且易碎的，需要解释系统来帮助用户进行调试。现有的模型解释系统善于以像素贡献的方式给出行为的字面解释，但无法整合可能影响预测的物理或系统过程的信息。本文引入了回归不连续设计这种简单形式的因果推理，用于将多个关键绩效指标的变化与物理世界现象相关联，以给用户提供更可行的视频分析解释集合。我们概述了系统架构，并描述了这种系统可能带来的影响的愿景。