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 Common Sense View of Knowledge Graphs | AI3:::Adaptive Information(About) > “Graph-based knowledge representation has been researched for decades and the term knowledge graph does not constitute a new technology. Rather, it is a buzzword reinvented by Google and adopted by other companies and academia to describe different knowledge representation applications.”
> ...knowledge graphs, like ontologies, have a broad range of applications & constructions. ...it is less important to reflect on some precise understanding as to realize that human language and knowledge is being presented in a connected, graph form
Ontotext | Semantic Technology Developer(About) > Ontotext transforms how organizations **identify meaning across** diverse databases and massive amounts of unstructured data by **combining a semantic graph database with text mining, and machine learning**.
Traversing Knowledge Graphs in Vector Space (2015)(About) Knowledge graphs often have missing facts (edges) which disrupts path queries. Recent models for knowledge base completion impute missing facts by embedding knowledge graphs in vector spaces. We show that these models can be recursively applied to answer path queries, but that they suffer from cascading errors. This motivates a new "compositional" training objective, which dramatically improves all models' ability to answer path queries, in some cases more than doubling accuracy.