Relational facts are an important component of human knowledge, which are hidden in vast
amounts of text. In order to extract these facts from text, people have been working on …

As an essential component of human cognition, cause–effect relations appear frequently in
text, and curating cause–effect relations from text helps in building causal networks for …

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

Open relation extraction is the task of extracting open-domain relation facts from natural
language sentences. Existing works either utilize heuristics or distant-supervised …

Continual relation learning aims to continually train a model on new data to learn
incessantly emerging novel relations while avoiding catastrophically forgetting old relations …

Abstract Knowledge graphs typically undergo open-ended growth of new relations. This
cannot be well handled by relation extraction that focuses on pre-defined relations with …

Recognizing relations between entities is a pivotal task of relational learning. Learning
relation representations from distantly-labeled datasets is difficult because of the abundant …

Open relation extraction (OpenRE) aims to extract novel relation types from open-domain
corpora, which plays an important role in completing the relation schemes of knowledge …

The clustering-based unsupervised relation discovery method has gradually become one of
the important methods of open relation extraction (OpenRE). However, high-dimensional …

Human activities and city routines follow patterns. Transfer learning can help achieve
scalable solutions toward the realization of smart cities accounting for similarities between …