In this paper, we consider advancing web-scale knowledge extraction and
alignment by integrating OpenIE extractions in the form of (subject, predicate,
object) triples with Knowledge Bases (KB). Traditional techniques from
universal schema and from schema mapping fall in two extremes: either they
perform instance-level inference relying on embedding for (subject, object)
pairs, thus cannot handle pairs absent in any existing triples; or they perform
predicate-level mapping and completely ignore background evidence from
individual entities, thus cannot achieve satisfying quality. We propose OpenKI
to handle sparsity of OpenIE extractions by performing instance-level
inference: for each entity, we encode the rich information in its neighborhood
in both KB and OpenIE extractions, and leverage this information in relation
inference by exploring different methods of aggregation and attention. In order
to handle unseen entities, our model is designed without creating
entity-specific parameters. Extensive experiments show that this method not
only significantly improves state-of-the-art for conventional OpenIE
extractions like ReVerb, but also boosts the performance on OpenIE from
semi-structured data, where new entity pairs are abundant and data are fairly
sparse.

本文提出了 OpenKI 模型，通过对 OpenIE 三元组和 Knowledge Bases（KB）的整合来处理 OpenIE 的稀疏性。该模型通过进行实例级别推理来处理未知实体，并借助聚合和关注机制在关系推理中利用其邻域中的信息。实验结果表明，该方法不仅显着提高了现有 OpenIE 提取的准确率，而且还提高了面向半结构化数据的 OpenIE 表现。