Equip** 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 …

Recent years have witnessed a proliferation of large-scale knowledge bases, including
Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase …

We show that it is feasible to perform entity linking by training a dual encoder (two-tower)
model that encodes mentions and entities in the same dense vector space, where candidate …

Abstract Knowledge Graphs (KGs) have been applied to many tasks including Web search,
link prediction, recommendation, natural language processing, and entity linking. However …

The large number of potential applications from bridging web data with knowledge bases
have led to an increase in the entity linking research. Entity linking is the task to link entity …

Abstract Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical
ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental …

With the large volume of new information created every day, determining the validity of
information in a knowledge graph and filling in its missing parts are crucial tasks for many …

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 …

Collaborative knowledge bases that make their data freely available in a machine-readable
form are central for the data strategy of many projects and organizations. The two major …

A typical architecture for end-to-end entity linking systems consists of three steps: mention
detection, candidate generation and entity disambiguation. In this study we investigate the …