With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

Continual learning on graph data has recently attracted paramount attention for its aim to
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …

As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to
identify identical entities across disparate knowledge graphs (KGs) by exploiting associated …

Knowledge graphs (KGs) have become valuable knowledge resources in various
applications, and knowledge graph embedding (KGE) methods have garnered increasing …

Ontology alignment is essential for data integration and interoperability across multiple
applications across diverse disciplines. In recent decades, significant advancements have …

Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs
(KGs), seeks to identify equivalent entity pairs across these graphs. Most existing …

How to identify those equivalent entities between knowledge graphs (KGs), which is called
Entity Alignment (EA), is a long-standing challenge. So far, many methods have been …

Aligning schemas and entities of community-created geographic data sources with
ontologies and knowledge graphs is a promising research direction for making this data …

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

Knowledge graphs (KGs) have become the standard technology for the representation of
factual information in applications such as recommendation engines, search, and question …