… and relation embeddings, and the outer level attempts to assign appropriate weights for
each entity and relation. Moreover, it is worth noting that our technique of applying weights to …

… Interestingly, most work on learning knowledge graph embeddings focuses entirely on
object properties, and, therefore, we will restrict our examples and model to those as well …

… models to enable them to learn weighted knowledge graph embeddings. In addtion, we
introduce weighted link prediction to evaluate the weighted knowledge graph embedding models…

… -based ranking objective functions to learn the model weights. There are also several work
[15, … Knowledge graph embedding targets at representing the highdimensional KG as latent …

… layers and to dynamically synthesize weights given an input. In … weights to process input
entities, and also achieve multi-task knowledge sharing across relations in the knowledge graph…

Knowledge graph (KG) embedding aims to study the embedding representation to retain
the inherent structure of KGs. Graph neural networks (GNNs), as an effective graph …

… knowledge graph embedding has been proposed and quickly gained massive attention [13],
[14], [15], [16], [17], [18], [19]. The key idea is to embed … , defined as the weighted average of …

… (b) By weighting embedding dimensions, we up-weighted y-… -based knowledge graph
embedding method with an … to characterise the embedding topologies and weights several …

… We first build a weighted graph such that the edge between node A and B is the number
of session that these two products have been co-viewed, co-purchased or substituted. For …

A knowledge graph (KG), also known as a knowledge base, … , knowledge graph embedding
is proposed to embed entities … yield model with abilities of knowledge inference and fusion. …