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 …

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

Abstract Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing
the world's structured knowledge and for integrating information extracted from multiple data …

There is now more data to analyze than ever before. As data volume and variety have
increased, so have the ties between machine learning and data integration become …

Structure information extraction refers to the task of extracting structured text fields from web
pages, such as extracting a product offer from a shopping page including product title …

This paper aims to incorporate travel time prediction in the next location prediction problem
to enable the prediction of the city-wide movement trajectory of an individual vehicle by …

Attribute value extraction refers to the task of identifying values of an attribute of interest from
product information. Product attribute values are essential in many e-commerce scenarios …

We present CASIE, a system that extracts information about cybersecurity events from text
and populates a semantic model, with the ultimate goal of integration into a knowledge …

The task of product attribute value extraction is to identify values of an attribute from product
information. Product attributes are important features, which help improve online shopping …

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 …