Leveraging Schema Labels to Enhance Dataset Search
Table 2: NDCG@k and Precision@k of different models on dataset retrieval. The superscript + shows statistically significant improvements for our SLMR model over other single and multifield document ranking models. T means title, D means description, DT means data table, G means generated schema labels.
['Method SDR', 'Used Fields T+D', 'NDCG@5 0.8920', '@10 0.8490', '@20 0.8222', '@50 0.8121', 'P@5 0.4122', '@10 0.3652', '@20 0.3452', '@50 0.3585']
Then we train a Random Forest with the learned schema label features. In practice, we could choose any multi-label classifier. For each column, we select the top 10 generated schema labels and filter those with probability lower than 0.5. For each dataset, we index the generated schema labels as an additional field. Note that, for Schema Label Mixed Ranking (SLMR), we trained three different models and the weights of used fields were forced to be non-zero in order to study the proposed research questions. The weights of used fields for multifield document representation are also set non-zero when optimizing the parameters. If a schema label mixed ranking model scores only on titles and descriptions (wl=0), it is equivalent to single-field ranking model scoring on titles and descriptions. With generated schema labels, the ranking model can have a higher performance on dataset retrieval task (Q2).