Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One of the popular ways to solve the KBQA problem is to make use of a pipeline of several NLP modules, including entity discovery and linking (EDL) and relation detection. Recent success on KBQA task usually involves complex network structures with sophisticated heuristics. Inspired by a previous work that builds a strong KBQA 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, WebQSP, and our newly created dataset, FreebaseQA. The experimental results show that FOFE-net performs well on KBQA subtasks, entity discovery and linking (EDL) and relation detection, and in turn pushing overall KB-QA system to achieve strong results on all the datasets
