Jun 10, 2020 · In this paper, we propose NAE (neighborhood aggregation embedding model), a novel approach for link prediction.

Link prediction has become a hot topic of knowledge graphs (KGs) in recent years. It aims at predicting missing links between entities to complement KGs.

Link prediction has become a hot topic of knowledge graphs (KGs) in recent years. It aims at predicting missing links between entities to complement KGs.

It aims at predicting missing links between entities to complement KGs. The most successful methods for this problem are embedding-based. Most previous works ...

Neighbor aggregation aims at aggregating the neighbors of an entity, and forming the neighbor-based embedding of the entity. Therefore, the embeddings of ...

Neighborhood Aggregation Embedding Model for Link Prediction in Knowledge Graphs ... Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge ...

These shallow embedding methods learn embeddings for each entity and relation and use a parameterized score function to predict the plau- sibility of a triple.

Aug 7, 2024 · This paper introduces the Node Centrality and Similarity Based Parameterised Model (NCSM), a novel method for link prediction tasks.

Apr 5, 2022 · We propose a new method for scaling training of knowledge graph embedding models for link prediction to address these challenges. Towards this ...

In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs.