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Extracting Optimal Data Snippets for Dataset Search and Beyond

This is the source code and data of the paper "Extracting Optimal Data Snippets for Dataset Search and Beyond".

Table of Contents

Directory Structure

The ./src directory contains all the source code, which is based on Python 3.10 and JDK 11.

  • ./src/OptimalDataSnippets contains the implementation of our method.
  • ./src/DatasetReranking contains the code for Experiment 1 (Dataset Reranking).
  • ./src/SnippetQuality contains the code for Experiment 2 (Snippet Quality).
  • ./src/ApproximationRatio contains the code for Experiment 3 (Approximation Ratio).
  • ./src/Utils contains classes and tools.

The ./data directory contains test collection data and results for reranking experiments.

  • ./data/test_collection_data/{test_collection} contains test collection data, including qrels, queries, and top-10 BM25 results.
  • ./data/results contains all the results for reranking experiments. Result files are named as {test_collection}_{reranking_model}_{snippet_extraction_method}_{topk}_{normalization_method}_{fusion_method}.txt and are in TREC format. For example, ntcir_bge_ours_top10_min-max_mixed.txt represents the reranking results of BGE with our snippet extraction method, min-max normalization, and mixed fusion method.
DS1-E-1001 Q0 f8cfaa69-3f89-4ebe-96e2-d15a30173f43 1 1.1313708498984762 mixed
DS1-E-1001 Q0 7629c1d5-5da8-45b5-bc8b-58483f97921a 2 0.8333792973031102 mixed
DS1-E-1001 Q0 ef605593-f1b3-40a4-a6a1-0c1fd2a4a381 3 0.7795421099910232 mixed

Requirements

  • pyserini==0.23.0
  • ranx==0.3.16
  • FlagEmbedding
  • tqdm
  • jsonlines
  • networkx
  • pandas
  • torch
  • transformers

Running Our Snippet Extraction Method

Getting Started

We conducted experiments on the following two test collections for ad hoc dataset retrieval:

NTCIR-E

NTCIR-E is the English version of the test collection used in the NTCIR-15 Dataset Search task, including 46,615 datasets and 192 queries.

ACORDAR

ACORDAR is a test collection specifically for RDF datasets, including 31,589 datasets and 493 queries.

We used the graph-based unification method from CDS to convert the data files of these two test collections into term.tsv and triple.tsv. Place them in the ./data/test_collection_data/{test_collection}/{filename} folder.

Use the following commands to prepare for executing our algorithm in ./src/OptimalDataSnippets:

python label_index_prepare.py
bash ./label_index_builder.py
python graph_init.py

The corresponding data and indexes will be generated in ./data/test_collection_data/{test_collection}/{filename} and ./data/index/{test_collection}/label_index, respectively.

Use the following command to extract our snippets for each query-data pair:

python extract_snippet.py

The resulting snippets will be in JSON files located in ./data/snippets, formatted as follows:

[
  {
    "rank_num": 1,
    "triples": "{subject} {predicate} {object}",
    "text": "xxx"
  },
  ...
]

Experiment 1: Dataset Reranking

We have placed all the code required for Experiment 1 in the ./src/DatasetReranking/{test_collection} directory. First, generate the training and test sets using the following commands:

python generate_bge_style_train_data.py
python generate_bge_style_dev_test_data.py

We implemented a fine-tuned reranking model for BGE and BGE-reranker based on code from the official GitHub repository. We use BAAI/bge-large-en-v1.5 and BAAI/bge-reranker-large as initial checkpoints, respectively. We followed the official instructions to mine hard negatives and then finetune both models according to the recommended method.

bash ./finetune_{test_collection}_data_ours_{ranking_model}.sh

We then calculated the relevance score of each <query, dataset> pair following the official instructions.

python test_{test_collection}_{ranking_model}.py

Run the following code to obtain the evaluation results:

python eval.py

Following the official guidelines, we directly used SFR and BGE-icl to calculate the relevance score of each <query, dataset> pair.

python test_{test_collection}_{ranking_model}.py

Run the following code to get the evaluation results:

python eval_7b.py

Experiment 2: Snippet Quality

Use the following commands to prepare for calculating quality metrics in ./src/SnippetQuality:

python component_index_prepare.py
bash ./component_index_prepare.py

Run the following code to get the evaluation results:

python quality_metrics.py

Experiment 3: Approximation Ratio

Run the following code to get the evaluation results in ./src/ApproximationRatio:

python cal_approximation_ratio.py

Other Algorithms

Please refer to the following repositories if you want to run other algorithms used in our experiments:

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.

Citation