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arXiv · 2008.08995

AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering

Seunghak Yu·Tianxing He·James Glass
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Abstract

Knowledge graphs (KGs) have the advantage of providing fine-grained detail for question-answering systems. Unfortunately, building a reliable KG is time-consuming and expensive as it requires human intervention. To overcome this issue, we propose a novel framework to automatically construct a KG from unstructured documents that does not require external alignment. We first extract surface-form knowledge tuples from unstructured documents and encode them with contextual information. Entities with similar context semantics are then linked through internal alignment to form a graph structure. This allows us to extract the desired information from multiple documents by traversing the generated KG without a manual process. We examine its performance in retrieval based QA systems by reformulating the WikiMovies and MetaQA datasets into a tuple-level retrieval task. The experimental results show that our method outperforms traditional retrieval methods by a large margin.

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Seunghak Yu, Tianxing He, James Glass. 2020-08-20. AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering. https://arxiv.org/abs/2008.08995

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