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 …

Relation extraction is a subtask of information extraction where semantic relationships are
extracted from natural language text and then classified. In essence, it allows us to acquire …

Challenging problems such as open-domain question answering, fact checking, slot filling
and entity linking require access to large, external knowledge sources. While some models …

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 …

Relation triples produced by open domain information extraction (open IE) systems are
useful for question answering, inference, and other IE tasks. Traditionally these are extracted …

With the evolution of the Internet and multimedia technology, the amount of text data has
increased exponentially. This text volume is a precious source of information and knowledge …

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 …

The DBpedia community project extracts structured, multilingual knowledge from Wikipedia
and makes it freely available on the Web using Semantic Web and Linked Data …

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) …

We present ELQ, a fast end-to-end entity linking model for questions, which uses a
biencoder to jointly perform mention detection and linking in one pass. Evaluated on …