A table is composed of data values that are organized in rows and columns providing
implicit structural information. A table is usually accompanied by secondary information such …

Ranking models are the main components of information retrieval systems. Several
approaches to ranking are based on traditional machine learning algorithms using a set of …

Data lakes (DLs) are large repositories of raw datasets from disparate sources. As more
datasets are ingested into a DL, there is an increasing need for efficient techniques to profile …

A search engine's ability to retrieve desirable datasets is important for data sharing and
reuse. Existing dataset search engines typically rely on matching queries to dataset …

Table retrieval is the task of extracting the most relevant tables to answer a user's query.
Table retrieval is an important task because many domains have tables that contain useful …

Generating schema labels automatically for column values of data tables has many data
science applications such as schema matching, and data discovery and linking. For …

Oceanographic research is a multidisciplinary endeavor that involves the acquisition of an
increasing amount of in-situ and remotely sensed data. A large and growing number of …

We describe the development, characteristics and availability of a test collection for the task
of Web table retrieval, which uses a large-scale Web Table Corpora extracted from the …

Tabular data plays an essential role in many data analytics and machine learning tasks.
Typically, tabular data does not possess any machine-readable semantics. In this context …

We address the problem of ad hoc table retrieval via a new neural architecture that
incorporates both semantic and relevance matching. Understanding the connection …