缺失的链接是可预测的吗？一种知识图谱完备化的推理基准
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion
摘要：
我们提出了InferWiki，一个知识图谱完成（KGC）数据集，提高了现有的基准在推理能力，假设和模式。首先，每个测试样本都是可预测的，并且在训练集中有支持数据。为了确保这一点，我们建议利用规则指导的训练/测试生成，而不是传统的随机分裂。其次，InferWiki在开放世界假设下启动评估，并通过提供手动注释的否定和未知三元组来提高封闭世界假设的推理难度。第三，我们包括各种推理模式（例如，推理路径长度和类型）进行综合评价。在实验中，我们策划了两个不同大小和结构的InferWiki设置，并将构建过程应用于CoDEx作为比较数据集。结果和实证分析证明了InferWiki的必要性和高质量。然而，不同的推理假设和模式之间的性能差距呈现出困难，并启发未来的研究方向。我们的数据集可以在https://github.com/TaoMiner/inferwiki上找到
英文摘要：
We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. To ensure it, we propose to utilize rule-guided train/test generation, instead of conventional random split. Second, InferWiki initiates the evaluation following the open-world assumption and improves the inferential difficulty of the closed-world assumption, by providing manually annotated negative and unknown triples. Third, we include various inference patterns (e.g., reasoning path length and types) for comprehensive evaluation. In experiments, we curate two settings of InferWiki varying in sizes and structures, and apply the construction process on CoDEx as comparative datasets. The results and empirical analyses demonstrate the necessity and high-quality of InferWiki. Nevertheless, the performance gap among various inferential assumptions and patterns presents the difficulty and inspires future research direction. Our datasets can be found in https://github.com/TaoMiner/inferwiki
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
