Leveraging Schema Labels to Enhance Dataset Search

Zhiyu Chen, Haiyan Jia, Jeff Heflin, and Brian D. Davison.

Full Paper (13 pages)
Official Springer published version: https://doi.org/10.1007/978-3-030-45439-5_18
Author's version: PDF (468KB)

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.

In Proceedings of 42nd European Conference on Information Retrieval (ECIR 2020), pages 267-280, April 2020.

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Last modified: 22 April 2020
Brian D. Davison