Ad Hoc Table Retrieval using Semantic Similarity

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.

arXiv commentThe web conference 2018 (WWW'18)

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Cited by in corpus (24)

• Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval
• Table Search Using a Deep Contextualized Language Model
• Correlation Sketches for Approximate Join-Correlation Queries
• Neural ranking models for document retrieval
• StruBERT: Structure-aware BERT for Table Search and Matching
• Retrieving Complex Tables with Multi-Granular Graph Representation Learning
• Novel Entity Discovery from Web Tables
• Auto-completion for Data Cells in Relational Tables
• Web Table Extraction, Retrieval and Augmentation: A Survey
• On-the-fly Table Generation
• Semantic Table Retrieval using Keyword and Table Queries
• WTR: A Test Collection for Web Table Retrieval
• Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System
• Dataset search: a survey
• Recommending Related Tables
• Simulating Users in Interactive Web Table Retrieval
• SmartTable: A Spreadsheet Program with Intelligent Assistance
• BERT Meets Relational DB: Contextual Representations of Relational Databases
• On Embeddings in Relational Databases
• A Graph Representation of Semi-structured Data for Web Question Answering
• CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering
• Automatic Table completion using Knowledge Base
• Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models
• Leveraging Schema Labels to Enhance Dataset Search