Knowledge graphs (KGs) model facts about the world, they consist of nodes
(entities such as companies and people) that are connected by edges (relations
such as founderOf). Facts encoded in KGs are frequently used by search
applications to augment result pages. When presenting a KG fact to the user,
providing other facts that are pertinent to that main fact can enrich the user
experience and support exploratory information needs. KG fact contextualization
is the task of augmenting a given KG fact with additional and useful KG facts.
The task is challenging because of the large size of KGs, discovering other
relevant facts even in a small neighborhood of the given fact results in an
enormous amount of candidates. We introduce a neural fact contextualization
method (NFCM) to address the KG fact contextualization task. NFCM first
generates a set of candidate facts in the neighborhood of a given fact and then
ranks the candidate facts using a supervised learning to rank model. The
ranking model combines features that we automatically learn from data and that
represent the query-candidate facts with a set of hand-crafted features we
devised or adjusted for this task. In order to obtain the annotations required
to train the learning to rank model at scale, we generate training data
automatically using distant supervision on a large entity-tagged text corpus.
We show that ranking functions learned on this data are effective at
contextualizing KG facts. Evaluation using human assessors shows that it
significantly outperforms several competitive baselines.

本文介绍了一个新的神经事实情境化方法（NFCM）来解决知识图谱事实情境化任务。 NFCM 首先在给定事实的附近生成候选事实集，然后使用监督学习来对候选事实进行排序。