A search engine's ability to retrieve desirable datasets is important for
data sharing and reuse. Existing dataset search engines typically rely on
matching queries to dataset descriptions. However, a user may not have enough
prior knowledge to write a query using terms that match with description
text.We propose a novel schema label generation model which generates possible
schema labels based on dataset table content. We incorporate the generated
schema labels into a mixed ranking model which not only considers the relevance
between the query and dataset metadata but also the similarity between the
query and generated schema labels. To evaluate our method on real-world
datasets, we create a new benchmark specifically for the dataset retrieval
task. Experiments show that our approach can effectively improve the precision
and NDCG scores of the dataset retrieval task compared with baseline methods.
We also test on a collection of Wikipedia tables to show that the features
generated from schema labels can improve the unsupervised and supervised web
table retrieval task as well.

本研究提出了一种新的基于数据集表格内容的模式标签生成模型，将该模型生成的模式标签与数据集元数据相结合，基于混合排序模型为数据集检索任务提供更高的检索准确率和 NDCG 得分，并在 Wikipedia 数据集上测试表明模式标签能够改善无监督和有监督的 Web 表格检索任务。