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Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion.
A generative network (GN) takes two elements of a (subject, predicate, object) triple as input and generates the vector representation of the missing element.
In this paper, we have explored the effects of different minibatch sampling techniques in Knowledge Graph Completion.
In this paper, we are concerned with two extensions of AnyBURL.
This paper addresses machine learning models that embed knowledge graph entities and relationships toward the goal of predicting unseen triples, which is an important task because most knowledge graphs are by nature incomplete.
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum.
Knowledge graph has gained increasing attention to recent years for its successful applications of numerous tasks.
We study theoretical properties of embedding methods for knowledge graph completion under the missing completely at random assumption.
However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features.
SOTA for Link Prediction on WN18RR