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

Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such
tables, there has been tremendous progress on a variety of tasks in the area of table …

We provide a comprehensive survey of the research literature that applies Information
Extraction techniques in a Semantic Web setting. Works in the intersection of these two …

Tabular data often refers to data that is organized in a table with rows and columns. We
observe that this data format is widely used on the Web and within enterprise data …

Tables are powerful and popular tools for organizing and manipulating data. A vast number
of tables can be found on the Web, which represent a valuable knowledge resource. The …

Data scientists are tasked with obtaining insights from data. However, suitable data is often
not immediately at hand, and there may be many potentially relevant datasets in a data lake …

Abstract Tabular data to Knowledge Graph matching is the process of assigning semantic
tags from knowledge graphs (eg, Wikidata or DBpedia) to the elements of a table. This task …

Automatically annotating column types with knowledge base (KB) concepts is a critical task
to gain a basic understanding of web tables. Current methods rely on either table metadata …

Dataset discovery from data lakes is essential in many real application scenarios. In this
paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes …