论文标题
MCMH：学习多链多跳链规则，用于知识图推理
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
论文作者
论文摘要
多跳上的推理方法在知识图上推断出具有多跳规则的实体之间的缺失关系，这与一系列关系相对应。我们扩展了现有的作品，以考虑多跳规则的广义形式，其中每个规则都是一组关系链。为了有效地学习这种广义规则，我们提出了一种两步方法，该方法通常首先选择一组关系链，然后通过共同评分选定的链来评估目标关系的信心。提出了一个游戏理论框架，以同时优化规则选择和预测步骤。经验结果表明，与标准的单链方法相比，我们的多链多跳跃（MCMH）规则取得了优越的结果，这证明了我们对广义规则的制定和拟议的学习框架的有效性。
Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.
