Relational databases are valuable resources for learning novel and interesting relations and concepts. In order to constraint the search through the large space of candidate definitions, users must tune the algorithm by specifying a language bias. Unfortunately, specifying the language bias is done via trial and error and is guided by the expert's intuitions. We propose AutoBias, a system that leverages information in the schema and content of the database to automatically induce the language bias used by popular relational learning systems. We show that AutoBias delivers the same accuracy as using manually-written language bias by imposing only a slight overhead on the running time of the learning algorithm.

中文翻译：

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使用自动语言偏差对关系数据进行可用和可扩展的学习

关系数据库是学习新颖有趣的关系和概念的宝贵资源。为了在候选定义的大空间中限制搜索，用户必须通过指定语言偏差来调整算法。不幸的是，指定语言偏差是通过反复试验完成的，并受专家直觉的指导。我们提出了 AutoBias，这是一个利用数据库模式和内容中的信息来自动诱导流行关系学习系统使用的语言偏见的系统。我们展示了 AutoBias 通过仅对学习算法的运行时间施加轻微的开销来提供与使用手动编写的语言偏差相同的准确性。