Query Answering with Soft Entity Constraints

This is the code repository accompanying the paper Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints.

Logical queries over knowledge graphs allow retrieving entities that meet constraints defined by logical formulas. In this project, we extend these with soft constraints that allow specifying that in addition to the logical constraints, an entity should be "like" or "unlike" specific exemplary entities:

If you find our work useful, please use the following citation:

@article{daza2025interactive,
  title={Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints},
  author={Daza, Daniel and Bernardi, Alberto and Costabello, Luca and Gueret, Christophe and Mansoury, Masoud and Cochez, Michael and Schut, Martijn},
  journal={arXiv preprint arXiv:2508.13663},
  year={2025}
}

Replicating our experiments

1. Installation

Use the following to create and environment with conda and install the requirements:

conda create -n nqr python=3.12
conda activate nqr
pip install -r requirements.txt

2. Download the data

The following are available upon request:

• Datasets
• Pretrained link prediction models
• Our raw experimental results data

3. Reproducing our experiments

The main entrypoint is the module nqr.qto.query. Given a dataset and a model, the module can train and test different methods for query answering with soft entity constraints. We provide configuration files for all our experiments in the configs directory. As an example, to test the Cosine method on the FB15k237 dataset, run

python -m nqr.qto.query --config configs/fb15k237/cosine.yaml

The generated results will be stored in the results directory.

4. Replicating our results

Since we provide raw experimental results, it is also possible to replicate the figures and tables in our paper without the need to repeat the experiments. To replicate Fig. 1, run

python analysis/plots.py

To replicate Table 3, run

python analysis/tables.py