|
基于嵌入模型的知识图规则学习
Rule Learning from Knowledge Graphs Guided by Embedding Models |
|
| |
| 课程网址: |
http://videolectures.net/iswc2018_stepanova_rule_learning/
|
| 主讲教师: |
Daria Stepanova
|
| 开课单位: |
马克斯·普朗克信息学研究所
|
| 开课时间: |
2018-11-22
|
| 课程语种: |
英语
|
| 中文简介: |
知识图上的规则（KG）捕获数据中的可解释模式，并提出了各种规则学习方法。由于KGs本质上是不完整的，所以可以使用规则来推断遗漏的事实。当KG合理完成时，学习规则的统计度量（如置信度）很好地反映了规则质量；然而，这些措施在其他方面可能具有误导性。因此，很难单独从KG中学习高质量的规则，并且可伸缩性要求只生成一小组候选规则。因此，候选规则的排序和修剪是一个主要问题。为了解决这个问题，我们提出了一种利用缺失事实的概率表示的规则学习方法。特别是，我们通过依赖来自KG和外部信息源（包括文本语料库）的预计算嵌入模型的反馈，迭代地扩展了从KG导出的规则。在真实世界的KGs上进行的实验证明了我们的新方法在学习规则的质量和它们产生的事实预测方面的有效性。
|
| 课程简介: |
Rules over a Knowledge Graph (KG) capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules is a major problem. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and external information sources including text corpora. Experiments on real-world KGs demonstrate the effectiveness of our novel approach both with respect to the quality of the learned rules and fact predictions that they produce.
|
| 关 键 词: |
基于嵌入模型; 知识图规则学习; 候选规则的排序和修剪; 统计度量
|
| 课程来源: |
视频讲座网
|
| 数据采集: |
2022-12-15：cyh
|
| 最后编审: |
2023-05-15：cyh
|
| 阅读次数: |
32
|
|
图 书 馆