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.
The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence
Berkman Klein Center（伯克曼·克莱因中心）；Harvard University（哈佛大学）；metaLAB；Data Nutrition Project（数据营养项目）；MIT Quest for Intelligence（麻省理工学院智能探索计划）；MIT（麻省理工学院）；Massachusetts College of Liberal Arts（马萨诸塞文理学院）
Leveraging Label Potential for Enhanced Multimodal Emotion Recognition
Xinjiang University（新疆大学）；School of Computer Science and Technology（计算机科学与技术学院）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center（新疆多模态智能处理与信息安全工程技术研究中心）
