Usable & Scalable Learning Over Relational Data With Automatic Language Bias

TL;DR

AutoBias automatically derives effective language biases from relational database schemas and content, enabling scalable and accurate relational learning without manual tuning.

Contribution

The paper introduces AutoBias, a system that automatically induces language bias for relational learning, reducing manual effort and maintaining accuracy.

Findings

AutoBias achieves comparable accuracy to manual bias.

AutoBias imposes only slight overhead on learning time.

AutoBias leverages schema and content information effectively.

Abstract

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.