Abstract Data Mining and Knowledge Discovery in Databases (KDD) is a research field
concerned with deriving higher-level insights from data. The tasks performed in that field are …

Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in
modeling graph-structured data. However, the primary focus has been on handling simple …

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …

Abstract Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their creation and …

In the recent years, different Web knowledge graphs, both free and commercial, have been
created. While Google coined the term “Knowledge Graph” in 2012, there are also a few …

Abstract Linked Open Data has been recognized as a valuable source for background
information in data mining. However, most data mining tools require features in propositional …

In this paper, we develop the Data Science Machine, which is able to derive predictive
models from raw data automatically. To achieve this automation, we first propose and …

Abstract Knowledge graphs are multi-relational data that contain massive entities and
relations. As an effective graph representation technique based on deep learning, graph …

We investigate Relational Graph Attention Networks, a class of models that extends non-
relational graph attention mechanisms to incorporate relational information, opening up …

Linked Open Data has been recognized as a valuable source for background information in
many data mining and information retrieval tasks. However, most of the existing tools require …