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

A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases