  Question answering over knowledge base (KB-QA) has recently become a popular
research topic in NLP. One popular way to solve the KB-QA problem is to make
use of a pipeline of several NLP modules, including entity discovery and
linking (EDL) and relation detection. Recent success on KB-QA task usually
involves complex network structures with sophisticated heuristics. Inspired by
a previous work that builds a strong KB-QA baseline, we propose a simple but
general neural model composed of fixed-size ordinally forgetting encoding
(FOFE) and deep neural networks, called FOFE-net to solve KB-QA problem at
different stages. For evaluation, we use two popular KB-QA datasets,
SimpleQuestions and WebQSP, and a newly created dataset, FreebaseQA. The
experimental results show that FOFE-net performs well on KB-QA subtasks, entity
discovery and linking (EDL) and relation detection, and in turn pushing overall
KB-QA system to achieve strong results on all datasets.
