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CMDL

Cross-Modal Data Discovery over Structured and Unstructured Data Lakes
Published at Very Large Databses (VLDB) 2023

Set up:

  • environment.yml will set up a conda environment

Entry points:

  • trainer/pretrain-text.ipynb: Fine tuning a language model on text corpus to learn text embeddings

  • trainer/pretrain-tables.ipynb: Fine tuning a language model on table collection to learn tuple embeddings

  • trainer/column_text_joint_training.ipynb: training a baseline connecting text to table columns

  • compare_gt.py: accuracy measurement of search based baselines and similarity sketches on text->table relation discovery using the ground truth provided

Data Sets & Ground Truths:

All files and directories are inside the inputs directory

  • #c5f015 Phamra

    • drugbank-tables: drugbank tables as csv files
    • pubmed-targets: pubmed article abstracts as txt files
    • DrugBank_Synthetic_dataset: synthetic drugbank tables as csv files

  • #f03c15 ChEBI

    • ChEBI_tables_dataset: ChEMBL tables as csv files
      Note: chebi-reference.csv.zip & chebi-structures.csv.zip are compressed due to GitHub limits

  • #1589F0 ChEMBL

    • ChEMBL_tables_dataset: ChEMBL tables as csv files
      Note: chembl_27-activity_supp.csv.zip , chembl_27-chembl_id_lookup.csv.zip , chembl_27-compound_records.csv.zip , chembl_27-molecule_dictionary.csv.zip are compressed due to GitHub limits

  • #ffffff MLOpen

    • MLOpen Data Source
    • For our experiments we use certain subsets of the data which can be found in the subdirectories:
      • mlopen_t2t_SS_dataset
      • mlopen_t2t_MS_dataset
      • mlopen_t2t_LS_dataset

  • #008000 UKOpen

The ground truth files for each dataset are present in the inputs directory

Resources:

  • Paper manuscripts provided under the folder 'docs'

Prior baselines:

  • snorkel labeler.ipynb needs to be run in its separate environment by following instructions at: https://github.com/snorkel-team/snorkel

  • build_label_files.py: profiles data, indexes tables, creates labels by probing indexes using each text

  • build_features.py: featurizes input data, saves features to disk to be read during training