Industrial tabular information extraction and its semantic fusion with text (ITIESF) is of great
significance in converting and fusing industrial unstructured data into structured knowledge …

Entity alignment—the discovery of identical entities across different knowledge graphs (KGs)—
is a critical task in data fusion. In this paper, we revisit existing entity alignment methods in …

Knowledge graph (KG) alignment is to match entities in different KGs, which is important to
knowledge fusion and integration. Temporal KGs (TKGs) extend traditional Knowledge …

Fusing data from different sources to improve decision making in smart cities has received
increasing attention. Collected data through sensors usually exist in a multi-modal form …

Advances in deep neural network (DNN) architectures have enabled new prediction
techniques for stock market data. Unlike other multivariate time-series data, stock markets …

Recently, many knowledge graph embedding models for knowledge graph completion have
been proposed, ranging from the initial translation-based models such as TransE to recent …

With recent advancements in graph neural networks (GNN), GNN-based recommender
systems (gRS) have achieved remarkable success in the past few years. Despite this …

The propagation of rumours on social media poses an important threat to societies, so that
various techniques for rumour detection have been proposed recently. Yet, existing work …

Entity Alignment (EA) is a crucial task in knowledge fusion, which aims to link entities with
the same real-world identity from different Knowledge Graphs (KGs). Existing methods have …

Entity alignment (EA) aims to automatically match entities in different knowledge graphs,
which is beneficial to the development of knowledge-driven applications. Representation …