Recently, knowledge representation learning (KRL) is emerging as the
state-of-the-art approach to process queries over knowledge graphs (KGs),
wherein KG entities and the query are embedded into a latent space such that
entities that answer the query are embedded close to the query. Yet, despite
the intensive research on KRL, most existing studies either focus on homogenous
KGs or assume KG completion tasks (i.e., inference of missing facts), while
answering complex logical queries over KGs with multiple aspects (multi-view
KGs) remains an open challenge.
To bridge this gap, in this paper, we present ROMA, a novel KRL framework for
answering logical queries over multi-view KGs. Compared with the prior work,
ROMA departs in major aspects. (i) It models a multi-view KG as a set of
overlaying sub-KGs, each corresponding to one view, which subsumes many types
of KGs studied in the literature (e.g., temporal KGs). (ii) It supports complex
logical queries with varying relation and view constraints (e.g., with complex
topology and/or from multiple views); (iii) It scales up to KGs of large sizes
(e.g., millions of facts) and fine-granular views (e.g., dozens of views); (iv)
It generalizes to query structures and KG views that are unobserved during
training. Extensive empirical evaluation on real-world KGs shows that \system
significantly outperforms alternative methods.

本文提出了一种基于知识表示学习的多视图知识图谱的逻辑查询解决方法，其中将多视图知识图谱建模为多个互相覆盖的子图，支持复杂的逻辑查询和大规模知识图谱，具有较好的实验效果。