An enormous amount of digital information is expressed as natural-language (NL) text that is
not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for …

Entity linking (EL) is the process of linking entity mentions appearing in web text with their
corresponding entities in a knowledge base. EL plays an important role in the fields of …

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go
beyond modeling user-item interactions and take side information into account. Traditional …

This paper introduces a conceptually simple, scalable, and highly effective BERT-based
entity linking model, along with an extensive evaluation of its accuracy-speed trade-off. We …

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed
alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model …

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by
linking the equivalent entities from various KGs. GNN-based EA methods present promising …

Entity alignment aims at integrating complementary knowledge graphs (KGs) from different
sources or languages, which may benefit many knowledge-driven applications. It is …

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 …

Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the
small scale of training data, previous methods perform poorly on unseen/sparsely labeled …