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.

中文翻译：

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使用关键字和表查询的语义表检索

Web 上的表格以结构化的形式包含大量知识。为了利用这个宝贵的资源，我们解决了表格检索的问题：通过表格的排序列表来满足信息需求。我们根据信息需求的表达方式，在两种不同的变体中调查这个问题：作为关键字查询或作为现有表（“逐表查询”）。这项工作的主要新颖贡献是一个语义表检索框架，用于将信息需求（关键字或表查询）与表匹配。具体来说，我们 (i) 在多个语义空间（离散稀疏和连续密集向量表示）中表示查询和表，并且 (ii) 引入各种相似性度量来匹配这些语义表示。我们考虑语义表示和相似性度量的所有可能组合，并将它们用作监督学习模型中的特征。使用基于 Wikipedia 表的两个专门构建的测试集合，我们展示了对最先进基线的显着和实质性改进。