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GOLDEN fusion: a graph-oriented learning with domain-embedding network fusion for generating super gene sets in functional genomics. [PDF]

Brief Bioinform
Li Q, Nichols C, Welner RS, Chen JY, Ku WS, Yue Z.   +5 more
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Knowledge graph embedding with concepts

Knowledge-Based Systems, 2019
Abstract Knowledge graph embedding aims to embed the entities and relationships of a knowledge graph in low-dimensional vector spaces, which can be widely applied to many tasks. Existing models for knowledge graph embedding primarily concentrate on entity–relation–entitytriplets, or interact with the text corpus.
Dandan Song, Lejian Liao
exaly   +2 more sources

Adversarial Explanations for Knowledge Graph Embeddings

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
We 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, Meilicke, Christian, Stuckenschmidt, Heiner   +2 more
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Knowledge Graph Embedding with Relation Constraint

2021
Knowledge 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, Xinghao Song, Hui Zhang 0055, Bo Li 0065   +3 more
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A Survey on Knowledge Graph Embedding

2022 7th IEEE International Conference on Data Science in Cyberspace (DSC), 2022
Qi Yan, Jiaxin Fan, Mohan Li, Guanqun Qu, Yang Xiao   +4 more
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Knowledge Graph Embedding by Bias Vectors

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019
Knowledge 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, Weiqin Tong, Xuehai Ding, Xiaoli Zhi, Xiao Wang 0023, Guoqing Zhang 0006   +5 more
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Exploring the Generalization of Knowledge Graph Embedding

2020
Knowledge 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, Huan Gao, Xianda Zheng, Guilin Qi, Jiming Liu 0005   +4 more
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Knowledge Graph Embedding with Triple Context

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
Knowledge 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, Huan Gao, Guilin Qi, Zhangquan Zhou   +3 more
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