Ad Hoc Table Retrieval using Semantic Similarity

TL;DR

This paper presents a novel semantic matching approach for ad hoc table retrieval, leveraging multiple semantic representations and similarity measures, resulting in significant improvements over existing methods.

Contribution

It introduces a new method for semantic matching in table retrieval using combined semantic spaces and similarity measures within a supervised learning framework.

Findings

Significant performance improvements over baseline methods.

Effective use of multiple semantic representations for matching.

Validated on Wikipedia tables with a purpose-built test collection.

Abstract

We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline.