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 …

The deployment of various networks (eg, Internet of Things [IoT] and mobile networks),
databases (eg, nutrition tables and food compositional databases), and social media (eg …

As graph databases become widespread, the International Organization for Standardization
(ISO) and International Electrotechnical Commission (IEC) have approved a project to create …

The aim of knowledge graph (KG) completion is to extend an incomplete KG with missing
triples. Popular approaches based on graph embeddings typically work by first representing …

This survey presents a comprehensive description of recent neural entity linking (EL)
systems developed since 2015 as a result of the “deep learning revolution” in natural …

Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but more …

Existing techniques for unionable table search define unionability using metadata (tables
must have the same or similar schemas) or column-based metrics (for example, the values …

Abstract Knowledge graph embeddings are now a widely adopted approach to knowledge
representation in which entities and relationships are embedded in vector spaces. In this …

Structured knowledge is important for many AI applications. Commonsense knowledge,
which is crucial for robust human-centric AI, is covered by a small number of structured …

Semantic embedding has been widely investigated for aligning knowledge graph (KG)
entities. Current methods have explored and utilized the graph structure, the entity names …