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 …

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 an essential task for semantic text understanding and information
extraction. Popular methods separately address the Mention Detection (MD) and Entity …

We propose a novel deep learning model for joint document-level entity disambiguation,
which leverages learned neural representations. Key components are entity embeddings, a …

Entity linking involves aligning textual mentions of named entities to their corresponding
entries in a knowledge base. Entity linking systems often exploit relations between textual …

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 …

The wide dissemination and misleading effects of online rumors on social media have
become a critical issue concerning the public and government. Detecting and regulating …

In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the
context of information retrieval (IR). Modern IR systems can benefit from information …

Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models
have achieved recent success in this task. However, most existing methods rely on local …

In this article, we focus on fake news detection task and aim to automatically identify the fake
news from vast amount of social media posts. To date, many approaches have been …