Knowledge from diverse application domains is organized as knowledge graphs
(KGs) that are stored in RDF engines accessible in the web via SPARQL
endpoints. Expressing a well-formed SPARQL query requires information about the
graph structure and the exact URIs of its components, which is impractical for
the average user. Question answering (QA) systems assist by translating natural
language questions to SPARQL. Existing QA systems are typically based on
application-specific human-curated rules, or require prior information,
expensive pre-processing and model adaptation for each targeted KG. Therefore,
they are hard to generalize to a broad set of applications and KGs.
In this paper, we propose KGQAn, a universal QA system that does not need to
be tailored to each target KG. Instead of curated rules, KGQAn introduces a
novel formalization of question understanding as a text generation problem to
convert a question into an intermediate abstract representation via a neural
sequence-to-sequence model. We also develop a just-in-time linker that maps at
query time the abstract representation to a SPARQL query for a specific KG,
using only the publicly accessible APIs and the existing indices of the RDF
store, without requiring any pre-processing. Our experiments with several real
KGs demonstrate that KGQAn is easily deployed and outperforms by a large margin
the state-of-the-art in terms of quality of answers and processing time,
especially for arbitrary KGs, unseen during the training.

本文提出了 KGQAn，一个通用的 QA 系统，它不需要为每个目标 KG 进行定制，而是采用一种新颖的形式化方法将问题理解为一个文本生成问题，并通过神经序列到序列模型将问题转换为中间抽象表示。我们还开发了一个即时链接器，在查询时间使用 RDF 存储的公共可访问 API 和现有索引将抽象表示映射到特定 KG 的 SPARQL 查询，而无需任何预处理。我们在几个真实的 KG 上进行的实验表明，KGQAn 易于部署，对于任意的 KG 表现良好，优于现有的 QA 系统。