This is the source code and data of the paper "Enhancing Dataset Search with Compact Data Snippets" SIGIR 2024.

Directory ./code contains all the source code based on Python 3.10 and JDK 11.

• Directory ./code/CBA contains code of the CBA algorithm which is aim to efficiently extract a subgraph as the skeleton of a data snippet. For the instructions of this part of the code, please refer to CBA.

• Directory ./code/CDS contains code for the other parts of our work. The code consists of the following sections:

  • graph_init.py reads from data graph which connecting entities associated with attributes and makes preparations for the execution of the CBA algorithm.
  • HBLL.sh builds HBLL for the CBA algorithm.
  • kw_init.py takes each word in the query as a separate group, and selects the entity nodes that meet the correlation requirements as nodes in this group according to the pre-constructed index.
  • CBA_runner.sh runs CBA algorithm.
  • attr_select.py selects attributes most relevant to the query based on the attribute index.
  • format_builder.py reverts unified data graphs to their original formats.

• Directory ./code/reranker contains code for dataset reranking including monoBERT, BGE, and BGE-reranker.

• File ./nltk_stopword.txt is a collection of common stop words provided by NLTK to filter queries.

In our experiment, each data consists of term.tsv and triple.tsv, and you need to pre-build the BGE indexes entities_bge-en-v1.5.index and edges_bge-en-v1.5.index based on them.

The parameter diameter bound is set to 4 and attribute number bound is set to 5 in this code.

Use following command to prepare for the execution of CBA algorithm:

python graph_init.py
bash ./HBLL.sh

Then graph.txt,nodeName.txt, tID_2_nID.txt andUWHBLL.txt will be generated.

Use following command to generate a separate folder for each Query-Data pair with previously generated data along with kwMap.txt, kwName.txt and query.txt:

python kw_init.py

The command for running CBA alorithm is similar:

bash ./CBA_runner.sh

Use following command to filter the attributes with the support of the attribute index:

python attr_select.py

Then query_graph_4_5.tsv will be generated, which is the unified graph format data snippet in our paper.

If you want to convert the snippet above to its original format, run the following code：

python format_builder.py

Then data snippet will be generated in the original format named query_result_4_5.tsv .

The complete structure of each data is as follows:

├─data
│  │  edges_bge-en-v1.5.index
│  │  entities_bge-en-v1.5.index
│  │  query.txt
│  │  term.tsv
│  │  triple.tsv
│  │
│  ├─CBA
│  │      graph.txt
│  │      nodeName.txt
│  │      tID_2_nID.txt
│  │      UWHBLL.txt
│  │
│  └─QID_0.55
│      │  graph.txt
│      │  kwMap.txt
│      │  kwName.txt
│      │  nodeName.txt
│      │  query.txt
│      │  tID_2_nID.txt
│      │  UWHBLL.txt
│      │
│      └─UW
│          └─CBA+result
│              └─0
│                      diameter_4_result.txt
│                      query_graph_4_5.tsv

All the results for reranking experiments are at ./results. The result files are named as {test_collection}_{reranking_model}_{snippet_extraction_method}_{topk}_{normalization_method}_{fusion_method}.txt and in TREC format. For example, ntcir_bge_cds_top10_min-max_mixed.txt means the reranking results of bge with CDS as snippet extraction method, min-max normalization as normalization method, and mixed fusion method.

DS1-E-1001 Q0 7629c1d5-5da8-45b5-bc8b-58483f97921a 1 1.1486173567566846 mixed
DS1-E-1001 Q0 f8cfaa69-3f89-4ebe-96e2-d15a30173f43 2 1.0281533671470748 mixed
DS1-E-1001 Q0 adaf0ce0-1064-4f55-9397-df553bd1ef75 3 0.925949423187341 mixed

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

@inproceedings{CDS,
  author       = {Qiaosheng Chen and
                  Jiageng Chen and
                  Xiao Zhou and
                  Gong Cheng},
  title        = {Enhancing Dataset Search with Compact Data Snippets},
  booktitle    = {Proceedings of the 47th International {ACM} {SIGIR} Conference on
                  Research and Development in Information Retrieval, {SIGIR} 2024, Washington
                  DC, USA, July 14-18, 2024},
  publisher    = {{ACM}},
  year         = {2024},
  url          = {https://doi.org/10.1145/3626772.3657837},
  doi          = {10.1145/3626772.3657837}
}