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Path-specific knowledge graph embedding
Knowledge-Based Systems, 2018Abstract Knowledge graph embedding aims to represent entities, relations and multi-step relation paths of a knowledge graph as vectors in low-dimensional vector spaces, and supports many applications, such as entity prediction, relation prediction, etc.
Yantao Jia
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Adversarial Explanations for Knowledge Graph Embeddings
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022We propose a novel black-box approach for performing adversarial attacks against knowledge graph embedding models. An adversarial attack is a small perturbation of the data at training time to cause model failure at test time. We make use of an efficient rule learning approach and use abductive reasoning to identify triples which are logical ...
Betz, Patrick +2 more
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Knowledge Graph Embedding with Relation Constraint
2021Knowledge graph (KG) is structure representations of the real-world facts by triples, and embedding entities and relations of a KG into continuous vector spaces is proven to be effective in many applications. Schema-based KG also has rich prior information about entities and relations, such as entity constraints for relations which define the semantic ...
Chunming Yang +3 more
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A Survey on Knowledge Graph Embedding
2022 7th IEEE International Conference on Data Science in Cyberspace (DSC), 2022Qi Yan +4 more
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Exploring the Generalization of Knowledge Graph Embedding
2020Knowledge graph embedding aims to represent structured entities and relations as continuous and dense low-dimensional vectors. With more and more embedding models being proposed, it has been widely used in many tasks such as semantic search, knowledge graph completion and intelligent question and answer.
Liang Zhang +4 more
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Knowledge Graph Embedding by Bias Vectors
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Knowledge graph completion can predict the possible relation between entities. Previous work such as TransE, TransR, TransPES and GTrans embed knowledge graph into vector space and treat relations between entities as translations. In most cases, the more complex the algorithm is, the better the result will be, but it is difficult to apply to large ...
Minjie Ding +5 more
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Knowledge Graph Embedding with Triple Context
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017Knowledge graph embedding, which aims to represent entities and relations in vector spaces, has shown outstanding performance on a few knowledge graph completion tasks. Most existing methods are based on the assumption that a knowledge graph is a set of separate triples, ignoring rich graph features, i.e., structural information in the graph.
Jun Shi +3 more
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Bias in Knowledge Graph Embeddings
2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020Styliani Bourli, Evaggelia Pitoura
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Federated knowledge graph completion via embedding-contrastive learning
Knowledge-Based Systems, 2022Huajun Chen, Mingyang Chen
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Learning knowledge graph embedding with a dual-attention embedding network
Expert Systems With Applications, 2023Yangdong Ye +2 more
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