BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph
Jingjing Xu, Maria Biryukov, Martin Theobald, Vinu Ellampallil, Venugopal

TL;DR
BigText-QA presents a hybrid knowledge graph approach that combines structured KBs and unstructured textual data to improve complex question answering, outperforming existing neural and graph-based systems.
Contribution
It introduces a unified hybrid knowledge graph for QA, integrating structured and unstructured knowledge sources for enhanced question answering capabilities.
Findings
Outperforms DrQA in accuracy
Achieves competitive results with QUEST
Effectively combines structured and unstructured knowledge
Abstract
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 worldsa canonical…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Semantic Web and Ontologies
