graknlabs/kglib

This project introduces a novel model: the Knowledge Graph Convolutional Network (KGCN). The principal idea of this work is to forge a bridge between knowledge graphs, automated logical reasoning, and machine learning, using Grakn as the knowledge graph. A KGCN can be used to create vector representations, embeddings, of any labelled set of Grakn Things via supervised learning. There are many benefits to storing complex and interrelated data in a knowledge graph, not least that the context of each datapoint can be stored in full. However, many existing machine learning techniques rely upon the existence of an input vector for each example.