论文标题
Autokg：从非结构化文档构建虚拟知识图以回答
AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering
论文作者
论文摘要
知识图（kgs）具有为提问系统提供细粒细节的优势。不幸的是，建造可靠的公里是耗时且昂贵的，因为它需要人类干预。为了克服这个问题，我们提出了一个新颖的框架，以自动从不需要外部对齐的非结构化文档中构造一个kg。我们首先从非结构化文档中提取表面形式知识元素，并用上下文信息对其进行编码。然后，具有相似上下文语义的实体通过内部对齐链接以形成图形结构。这使我们能够通过无需手动流程即可从多个文档中提取所需的信息。我们通过将Wikimovies和Metaqa数据集重新设计为元组级检索任务，从而在基于检索的质量检查系统中的性能进行了研究。实验结果表明，我们的方法的表现优于传统的检索方法。
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.
