JECI: A Joint Knowledge Graph Embedding Model for Concepts and Instances
Abstract: 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 coarse-grained to the fine-grained. 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 state-of-the-art models in most cases.
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