CIST: Differentiating Concepts and Instances Based on Spatial Transformation for Knowledge Graph Embedding

Knowledge representation learning is representing entities and relations in a knowledge graph as dense low-dimensional vectors in the continuous space, which explores the features and properties of the graph. Such a technique can facilitate the computation and reasoning on the knowledge graphs, which benefits many downstream tasks. In order to alleviate the problem of insufficient entity representation learning caused by sparse knowledge graphs, some researchers propose knowledge graph embedding models based on instances and concepts, which utilize the latent semantic connections between concepts and instances contained in the knowledge graphs to enhance the knowledge graph embedding. However, they model instances and concepts in the same space or ignore the transitivity of isA relations, leading to inaccurate embeddings of concepts and instances. To address the above shortcomings, we propose a knowledge graph embedding model that differentiates concepts and instances based on spatial transformation—CIST. The model alleviates the gathering issue of similar instances or concepts in the semantic space by modeling them in different embedding spaces, and adds a learnable parameter to adjust the neighboring range for concept embedding to distinguish hierarchical information of different concepts, thus modeling the transitivity of isA relations. The above features of instances and concepts serve as auxiliary information so that thoroughly modeling them could alleviate the insufficient entity representation learning issue. For the experiments, we chose two tasks, i.e., link prediction and triple classification, and two real-life datasets: YAGO26K-906 and DB111K-174. Compared with state of the arts, CIST achieves an optimal performance in most cases. Specifically, CIST outperforms the SOTA model JOIE by 51.1% on Hits@1 in link prediction and 15.2% on F1 score in triple classification.