Lifted Rule Injection for Relation Embeddings
Thomas R. DeMeester, Tim Rocktäschel, Sebastian Riedel · 2016
Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks.Yet, a major challenge is how to efficiently incorporate commonsense knowledge into such models.A recent approach regularizes relation and entity representations by propositionalization of first-order logic rules.However, propositionalization does not scale beyond domains with only few entities and rules.In this paper we present a highly efficient method for incorporating implication rules into distributed representations for automated knowledge base construction.We map entity-tuple embeddings into an approximately Boolean space and encourage a partial ordering over relation embeddings based on implication rules mined from WordNet.Surprisingly, we find that the strong restriction of the entity-tuple embedding space does not hurt the expressiveness of the model and even acts as a regularizer that improves generalization.By incorporating few commonsense rules, we achieve an increase of 2 percentage points mean average precision over a matrix factorization baseline, while observing a negligible increase in runtime.