Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Knowledge graphs are widely used in industry and studied within the academic community.
However, the models applied in the development of knowledge graphs vary. Analysing and …

Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs
(KGs), which is an essential step to enhance the quality of KGs, and hence of significance to …

Entity matching (EM) finds data instances that refer to the same real-world entity. Most EM
solutions perform blocking then matching. Many works have applied deep learning (DL) to …

Entity Resolution (ER) lies at the core of data integration and cleaning and, thus, a bulk of
the research examines ways for improving its effectiveness and time efficiency. The initial …

Entity Matching is the task of deciding if two entity descriptions refer to the same real-world
entity. State-of-the-art entity matching methods often rely on fine-tuning Transformer models …

An increasing number of data providers have adopted shared numbering schemes such as
GTIN, ISBN, DUNS, or ORCID numbers for identifying entities in the respective domain. This …

Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten
years. Building on a storied tradition of graphs in the AI community, a KG may be simply …

Neural methods have become the de-facto choice for the vast majority of data analysis tasks,
and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA …

More and more weakly structured, and irregular data sources are becoming available every
day. The schema of these sources is useful for a number of tasks, such as query answering …