Transfer Learning from Minimal Target Data by Mapping across Relational Domains

A central goal of transfer learning is to enable learning when training data from the domain of interest is limited. Yet, work on transfer across relational domains has so far focused on the case where there is a significant amount of target data. This paper bridges this gap by studying transfer when the amount of target data is minimal and consists of information about just a handful of entities. In the extreme case, only a single entity is known. We present the SR2LR algorithm that finds an effective mapping of predicates from a source model to the target domain in this setting and thus renders preexisting knowledge useful to the target task. We demonstrate SR2LR's effectiveness in three benchmark relational domains on social interactions and study its behavior as information about an increasing number of entities becomes available.