Question Answering over Knowledge Base using Factual Memory Networks

In the task of question answering, Memory Networks have recently shown to be quite effective towards complex reasoning as well as scalability, in spite of limited range of topics covered in training data. In this paper, we introduce Factual Memory Network, which learns to answer questions by extracting and reasoning over relevant facts from a Knowledge Base. Our system generate distributed representation of questions and KB in same word vector space, extract a subset of initial candidate facts, then try to find a path to answer entity using multi-hop reasoning and refinement. Additionally, we also improve the run-time efficiency of our model using various computational heuristics.