Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Entities are at the center of how we represent and aggregate knowledge. For instance,
Encyclopedias such as Wikipedia are structured by entities (eg, one per Wikipedia article) …

Entity matching (EM) finds data instances that refer to the same real-world entity. In this
paper we examine applying deep learning (DL) to EM, to understand DL's benefits and …

Selection of text feature item is a basic and important matter for text mining and information
retrieval. Traditional methods of feature extraction require handcrafted features. To hand …

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

Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity
mentions in a document to their correct references in a knowledge base (KB)(eg, Wikipedia) …

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 …

Instant analysis of cybersecurity reports is a fundamental challenge for security experts as
an immeasurable amount of cyber information is generated on a daily basis, which …