This repository contains the code for our demo publication at SIGMOD'22. We continued the project as WannaDB.

ASET: Ad-hoc Structured Exploration of Text Collections

ASET extracts information nuggets from a collection of textual documents and matches them to a list of user-specified attributes. Watch our demo video or read our demo paper to learn more about the usage and underlying concepts.

Run main.py to start the ASET GUI.

Data Sets

The evaluation scripts use multiple data sets to evaluate the system.

• The aviation data set consists of executive summaries from the NTSB Aviation Accident Reports.
• The corona data set consists of summaries of the RKI's daily reports about the Covid-19 situation in Germany.
• The countries data set consists of articles about sovereign states from the T-REx dataset
• The nobel data set consists of articles about nobel price winners from the T-REx dataset
• The skyscrapers data aset consists of articles about skyscrapers from the T-REx dataset

For each data set, there is a handler module that is meant to access the data set. The annotated documents of the data set are stored as *.json files in the documents folder. The raw documents are stored as *.txt files in the raw-documents folder.

Set up the Project

This project runs with Python 3.9.7.

Check out the requirements.txt file to see which packages have to be installed.

You can install them with pip install -r requirements.txt.

You may have to install torch by hand if you want to use CUDA:

https://pytorch.org/get-started/locally/

Make sure to also install pytest to execute the tests.

Citation

If you use ASET for scientific purposes, please cite it as follows:

@inproceedings{haettasch22ASET,
  author = {H\"{a}ttasch, Benjamin and Bodensohn, Jan-Micha and Binnig, Carsten},
  title = {Demonstrating ASET: Ad-Hoc Structured Exploration of Text Collections},
  year = {2022},
  isbn = {9781450392495},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3514221.3520174},
  doi = {10.1145/3514221.3520174},
  abstract = {In this demo, we present ASET, a novel tool to explore the contents of unstructured data (text) by automatically transforming relevant parts into tabular form. ASET works in an ad-hoc manner without the need to curate extraction pipelines for the (unseen) text collection or to annotate large amounts of training data. The main idea is to use a new two-phased approach that first extracts a superset of information nuggets from the texts using existing extractors such as named entity recognizers. In a second step, it leverages embeddings and a novel matching strategy to match the extractions to a structured table definition as requested by the user. This demo features the ASET system with a graphical user interface that allows people without machine learning or programming expertise to explore text collections efficiently. This can be done in a self-directed and flexible manner, and ASET provides an intuitive impression of the result quality.},
  booktitle = {Proceedings of the 2022 International Conference on Management of Data},
  pages = {2393–2396},
  numpages = {4},
  keywords = {interactive text exploration, matching embeddings, text to table},
  location = {Philadelphia, PA, USA},
  series = {SIGMOD '22}
}