Semantic Table Retrieval Using Keyword and Table Queries

Author:

Zhang Shuo1,Balog Krisztian2

Affiliation:

1. Bloomberg, United Kindom

2. University of Stavanger, Stavanger, Norway

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Large Scale Test Corpus for Semantic Table Search;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

2. COTER: Conditional Optimal Transport meets Table Retrieval;Proceedings of the 17th ACM International Conference on Web Search and Data Mining;2024-03-04

3. Evaluating the Impact of Content Deletion on Tabular Data Similarity and Retrieval Using Contextual Word Embeddings;Lecture Notes in Computer Science;2024

4. Table Question Answering Method for Engineering Domain Based on Multi-Dimensional Semantic Information;2024

5. GXJoin: Generalized Cell Transformations for Explainable Joinability;Lecture Notes in Computer Science;2024

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