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 …

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs
have firmly established themselves as valuable sources of information for search and …

Commonsense knowledge (CSK) about concepts and their properties is helpful for AI
applications. Prior works, such as ConceptNet, have compiled large CSK collections …

Automatically extracting information from unstructured text in pharmaceutical documents is
important for drug discovery and development. This information can be integrated with …

Open Information Extraction (OIE) systems seek to compress the factual propositions of a
sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in …

Open information extraction (OIE) is the task of extracting relations and their corresponding
arguments from a natural language text in un-supervised manner. Outputs of such systems …

Abstract Open Relation Extraction (ORE) task remains a challenge to obtain a semantic
representation by discovering arbitrary relations from the unstructured text. Conventional …

Structured knowledge bases (KBs) are the backbone of many knowledge-intensive
applications, and their automated construction has received considerable attention. In …

Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate
tasks like question answering. We study relation integration that aims to align free-text …

Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on
the prediction of a relational predicate from a natural-language input unit (such as a …