Answering complex questions over textual resources remains a challenge, particularly when dealing with nuanced relationships between multiple entities expressed within natural-language sentences. To this end, curated knowledge bases (KBs) like YAGO, DBpedia, Freebase, and Wikidata have been widely used and gained great acceptance for question-answering (QA) applications in the past decade. While these KBs offer a structured knowledge representation, they lack the contextual diversity found in natural-language sources. To address this limitation, BigText-QA introduces an integrated QA approach, which is able to answer questions based on a more redundant form of a knowledge graph (KG) that organizes both structured and unstructured (i.e., "hybrid") knowledge in a unified graphical representation. Thereby, BigText-QA is able to combine the best of both worlds$\unicode{x2013}$a canonical set of named entities, mapped to a structured background KB (such as YAGO or Wikidata), as well as an open set of textual clauses providing highly diversified relational paraphrases with rich context information. Our experimental results demonstrate that BigText-QA outperforms DrQA, a neural-network-based QA system, and achieves competitive results to QUEST, a graph-based unsupervised QA system.

翻译：回答基于文本资源的复杂问题仍然是一项挑战，尤其是在处理自然语句中多个实体之间表达的细微关系时。为此，在过去十年中，像YAGO、DBpedia、Freebase和Wikidata这样的策展知识库（KBs）被广泛使用，并在问答（QA）应用中获得了广泛认可。尽管这些知识库提供了结构化的知识表示，但它们缺乏自然语言来源中的上下文多样性。为了解决这一局限，BigText-QA引入了一种集成的问答方法，该方法能够基于一种更冗余形式的知识图谱（KG）来回答问题，这种知识图谱将结构化和非结构化（即“混合”）知识组织在统一的图形表示中。因此，BigText-QA能够结合两者的优势——规范化的命名实体集，映射到结构化背景KB（如YAGO或Wikidata），以及开放的文本子句集，提供高度多样化的关系释义和丰富的上下文信息。我们的实验结果表明，BigText-QA优于基于神经网络的问答系统DrQA，并与基于图的非监督问答系统QUEST取得了具有竞争力的结果。