论文标题
正则疑问是在不完整的知识基础上查询
Regex Queries over Incomplete Knowledge Bases
论文作者
论文摘要
我们建议在不完整的KBS上回答正则表达查询（包含脱节（$ \ vee $）和Kleene plus（$+$）操作员）的新颖任务。这些查询的答案集可能具有大量实体，因此，在KBC中，以前用于单跳查询的工作，将查询作为高维空间中的一个点并不那么有效。作为响应，我们开发了旋转框 - 旋转和盒子嵌入的新型组合。与现有基于嵌入的模型相比，它可以建模更多的关系推理模式。此外，我们定义了用于嵌入基线的KBC模型来处理正则运算符的基线方法。我们证明了本文介绍的两个新的Regex-Query数据集上的旋转框的性能，其中包括根据实际用户查询日志收集查询的一个。我们发现，我们的最终旋转框模型明显胜过基于旋转和仅框嵌入的模型。
We propose the novel task of answering regular expression queries (containing disjunction ($\vee$) and Kleene plus ($+$) operators) over incomplete KBs. The answer set of these queries potentially has a large number of entities, hence previous works for single-hop queries in KBC that model a query as a point in high-dimensional space are not as effective. In response, we develop RotatE-Box -- a novel combination of RotatE and box embeddings. It can model more relational inference patterns compared to existing embedding based models. Furthermore, we define baseline approaches for embedding based KBC models to handle regex operators. We demonstrate performance of RotatE-Box on two new regex-query datasets introduced in this paper, including one where the queries are harvested based on actual user query logs. We find that our final RotatE-Box model significantly outperforms models based on just RotatE and just box embeddings.
