Semantic Table Retrieval using Keyword and Table Queries
This addresses the challenge of accessing structured knowledge from web tables for users needing information retrieval, representing an incremental advancement in semantic matching techniques.
The paper tackles the problem of retrieving relevant tables from the web using keyword or table queries by proposing a semantic framework that represents queries and tables in multiple semantic spaces and uses various similarity measures, achieving significant improvements over state-of-the-art baselines on Wikipedia-based test collections.
Tables on the Web contain a vast amount of knowledge in a structured form. To tap into this valuable resource, we address the problem of table retrieval: answering an information need with a ranked list of tables. We investigate this problem in two different variants, based on how the information need is expressed: as a keyword query or as an existing table ("query-by-table"). The main novel contribution of this work is a semantic table retrieval framework for matching information needs (keyword or table queries) against 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 two purpose-built test collections based on Wikipedia tables, we demonstrate significant and substantial improvements over state-of-the-art baselines.