Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach

A standard pipeline for statistical rela-tional learning involves two steps: one first constructs the knowledge base (KB) from text, and then performs the learn-ing and reasoning tasks using probabilis-tic first-order logics. However, a key is-sue is that information extraction (IE) er-rors from text affect the quality of the KB, and propagate to the reasoning task. In this paper, we propose a statistical rela-tional learning model for joint information extraction and reasoning. More specifi-cally, we incorporate context-based entity extraction with structure learning (SL) in a scalable probabilistic logic framework. We then propose a latent context inven-tion (LCI) approach to improve the per-formance. In experiments, we show that our approach outperforms state-of-the-art baselines over three real-world Wikipedia datasets from multiple domains; that joint learning and inference for IE and SL sig-nificantly improve both tasks; that latent context invention further improves the re-sults.