Tables are probably the most natural way to represent relational data in various media and
formats. They store a large number of valuable facts that could be utilized for question …

Inferring meta information about tables, such as column headers or relationships between
columns, is an active research topic in data management as we find many tables are …

Detecting the semantic types of data columns in relational tables is important for various
data preparation and information retrieval tasks such as data cleaning, schema matching …

Existing techniques for unionable table search define unionability using metadata (tables
must have the same or similar schemas) or column-based metrics (for example, the values …

Table understanding methods extract, transform, and interpret the information contained in
tabular data embedded in documents/files of different formats. Such automatic …

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 …

Machine learning (ML) is playing an increasingly important role in data management tasks,
particularly in Data Integration and Preparation (DI&P). The success of ML-based …

Information extraction from semi-structured webpages provides valuable long-tailed facts for
augmenting knowledge graph. Relational Web tables are a critical component containing …

Modern data lakes are heterogeneous in the vocabulary that is used to describe data. We
study a problem of disambiguation in data lakes: How can we determine if a data value …

Modern data lakes are deeply heterogeneous in the vocabulary that is used to describe
data. We study a problem of disambiguation in data lakes: how can we determine if a data …