A2N: Attending to Neighbors for Knowledge Graph Inference - ACL 2019(About) > State-of-the-art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multi-relational graph which can generalize to infer unseen entity relationships at test time. This can be sub-optimal as it requires memorizing and generalizing to all possible entity relationships using these fixed representations. We thus propose a novel **attention-based method to learn query-dependent representation of entities** which adaptively combines the relevant graph neighborhood of an entity leading to more accurate KG completion.
A Hybrid Graph Model for Distant Supervision Relation Extraction | Springer for Research & Development (ESWC 2019)(About) > Distant supervision has advantages of generating training data automatically for relation extraction by aligning triples in Knowledge Graphs with large-scale corpora. >... we propose a novel hybrid graph model, which can incorporate heterogeneous background information in a unified framework, such as entity types and human-constructed triples. These various kinds of knowledge can be integrated efficiently even with several missing cases. In addition, we further employ an attention mechanism to identify the most confident information which can alleviate the side effect of noise.