In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

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 …

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 …

We design and release BONIE, the first open numerical relation extractor, for extracting
Open IE tuples where one of the arguments is a number or a quantity-unit phrase. BONIE …

Relational HTML tables on the Web contain data describing a multitude of entities and
covering a wide range of topics. Thus, web tables are very useful for filling missing values in …

In recent years, there has been an increasing interest in extracting and annotating tables on
the Web. This activity allows the transformation of text data into machine-readable formats to …

Given a large amount of table data, how can we find the tables that contain the contents we
want? A naive search fails when the column names are ambiguous, such as if columns …

Abstract Knowledge Graphs (KGs) currently contain a vast amount of structured information
in the form of entities and relations. Because KGs are often constructed automatically by …

Supervised machine learning is the task of learning a function that maps features to a target.
The strength of that function or the model depends directly on the features provided to the …

We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables.
Existing techniques tend to interpret tables by focusing on information that is already in the …