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

arXiv: https://arxiv.org/abs/2008.08995 · HTML: https://mlsanity.com/paper/2008.08995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory](https://mlsanity.com/paper/2209.07928/) (2022)

## Abstract (arXiv)

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.