Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …

Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and
related applications, which aims to complete the structure of knowledge graph by predicting …

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 Graphs (KGs) have found many applications in industrial and in academic
settings, which in turn, have motivated considerable research efforts towards large-scale …

Knowledge graphs are important resources for many artificial intelligence tasks but often
suffer from incompleteness. In this work, we propose to use pre-trained language models for …

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial
information, in the form of missing relations (links) between entities, has fueled a lot of …

We study the problem of learning representations of entities and relations in knowledge
graphs for predicting missing links. The success of such a task heavily relies on the ability of …

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …

Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge
graph (KG) to answer natural language questions. It helps end users more efficiently and …