JECI: A Joint Knowledge Graph Embedding Model for Concepts and Instances Concepts and instances are important parts in knowledge graphs, but most knowledge graph embedding models treat them as entities equally, that leads to inaccurate embeddings of concepts and instances. Aiming to address this problem, we propose a novel knowledge graph embedding model called JECI to jointly embed concepts and instances. First, JECI organizes concepts in the knowledge graph as a hierarchical tree, which maps concepts to a tree. Meanwhile, for an instance, JECI generates a context vector to represent the neighbor context in the knowledge graph. Then, based on the context vector and supervision information generated from the hierarchical tree, an embedding learner is designed to precisely locate an instance in embedding space from the coarsegrained to the finegrained. A prediction function, as the form of convolution, is designed to predict concepts of different granularities that an instance belongs to. In this way, concepts and instances are jointly embedded, and hierarchical structure is preserved in embedds. Especially, JECI can handle the complex relation by incorporating neighbor information of instances. JECI is evaluated by link prediction and triple classification on real world data. Experimental results demonstrate that it outperforms stateoftheart models in most cases.

Knowledge Graph Fact Prediction Via KnowledgeEnriched Tensor Factorization Abstract   We present a family of novel methods for embedding knowledge graphs into realvalued tensors. These tensorbased embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a provablyconvergent, linear tensor factorization algorithm. We demonstrate the efficacy of our models for the task of predicting new facts across eight different knowledge graphs, achieving between 5% and 50% relative improvement over existing stateoftheart knowledge graph embedding techniques. Our empirical evaluation shows that all of the tensor decomposition models perform well when the average degree of an entity in a graph is high, with constraintbased models doing better on graphs with a small number of highly similar relations and regularizationbased models dominating for graphs with relations of varying degrees of similarity.