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.
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