论文标题
KGXBoard：可解释的互动排行榜，用于评估知识图完成模型
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
论文作者
论文摘要
知识图（kgs）以（头，谓词，尾部） - 轨道的形式存储信息。为了增强具有新知识的公斤，研究人员提出了诸如链接预测之类的KG完成（KGC）任务的模型；即回答（H; P;？）或（？; P; t）查询。这种模型通常通过持有测试集的平均指标进行评估。尽管对于跟踪进度有用，但平均单分数指标无法透露模型所学或未能学习的内容。为了解决这个问题，我们提出了KGXBoard：一个交互式框架，用于对有意义的数据子集进行精细粒度评估，每个框架都测试了KGC模型的个人和可解释功能。在我们的实验中，我们强调了使用KGXBoard发现的发现，这是无法通过标准平均单分数指标来检测的。
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p; ?) or (?; p; t) queries. Such models are usually evaluated with averaged metrics on a held-out test set. While useful for tracking progress, averaged single-score metrics cannot reveal what exactly a model has learned -- or failed to learn. To address this issue, we propose KGxBoard: an interactive framework for performing fine-grained evaluation on meaningful subsets of the data, each of which tests individual and interpretable capabilities of a KGC model. In our experiments, we highlight the findings that we discovered with the use of KGxBoard, which would have been impossible to detect with standard averaged single-score metrics.
