Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs
SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80{\%} which sets a new state-of-the-art on the SimpleQuestions dataset.
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Entity LinkingInformation RetrievalKnowledge GraphsLearning-To-RankQuestion AnsweringRelation PredictionRe-RankingRetrievalTripletMethods 이 논문이 사용한 방법론
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