Scientific Dataset Discovery via Topic-level Recommendation
This addresses the time-consuming challenge of dataset discovery for researchers in data-intensive fields, though it appears incremental as it builds on existing graph-based methods with a topic-level approach.
The paper tackles the problem of discovering relevant scientific datasets for research projects by modeling dataset discovery on an attributed heterogeneous graph and using shared latent topics to characterize papers and datasets. The results show that the model generates reasonable dataset profiles and recommends proper datasets for queries, with experimental validation.
Data intensive research requires the support of appropriate datasets. However, it is often time-consuming to discover usable datasets matching a specific research topic. We formulate the dataset discovery problem on an attributed heterogeneous graph, which is composed of paper-paper citation, paper-dataset citation, and also paper content. We propose to characterize both paper and dataset nodes by their commonly shared latent topics, rather than learning user and item representations via canonical graph embedding models, because the usage of datasets and the themes of research projects can be understood on the common base of research topics. The relevant datasets to a given research project can then be inferred in the shared topic space. The experimental results show that our model can generate reasonable profiles for datasets, and recommend proper datasets for a query, which represents a research project linked with several papers.