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 …

Generating value from data requires the ability to find, access and make sense of datasets.
There are many efforts underway to encourage data sharing and reuse, from scientific …

Table-based reasoning has shown remarkable progress in a wide range of table-based
tasks. It is a challenging task, which requires reasoning over both free-form natural language …

Tables contain valuable knowledge in a structured form. We employ neural language
modeling approaches to embed tabular data into vector spaces. Specifically, we consider …

When working with any sort of knowledge base (KB) one has to make sure it is as complete
and also as up-to-date as possible. Both tasks are non-trivial as they require recall-oriented …

When conducting literature reviews, scientists often create literature review tables-tables
whose rows are publications and whose columns constitute a schema, a set of aspects used …

We address the task of auto-completing data cells in relational tables. Such tables describe
entities (in rows) with their attributes (in columns). We present the CellAutoComplete …

Entity set expansion and synonym discovery are two critical NLP tasks. Previous studies
accomplish them separately, without exploring their interdependencies. In this work, we …

Predicting the performance of a retrieval method for a given query is a highly important and
challenging problem in information retrieval. Accurate Query Performance Prediction (QPP) …

Knowledge Graph Augmentation is the task of adding missing facts to an incomplete
knowledge graph to improve its effectiveness in applications such as web search and …