Installation

pip install ontosample

# 1. clonegit clone https://github.com/alkidbaci/OntoSample.git# 2. setup virtual environmentpython -m venv venv# 3. activate the virtual environmentsource venv/bin/activate# for Unix and macOS.\venv\Scripts\activate# for Windows# 4. install dependenciespip install -r requirements.txt

Usage

fromontolearn_light.knowledge_baseimportKnowledgeBasefromontosample.classic_samplersimportRandomNodeSampler# 1. Initialize KnowledgeBase object using the path of the ontologykb=KnowledgeBase(path="KGs/Family/family-benchmark_rich_background.owl")# 2. Initialize the sampler and generate the samplesampler=RandomNodeSampler(kb)sampled_kb=sampler.sample(30)# will generate a sample with 30 nodes# 3. Save the sampled ontologysampler.save_sample(kb=sampled_kb,filename='sampled_kb')

Citing

@inproceedings{10.1145/3583780.3615158,author = {Baci, Alkid and Heindorf, Stefan},title = {Accelerating Concept Learning via Sampling},year = {2023},isbn = {9798400701245},publisher = {Association for Computing Machinery},address = {New York, NY, USA},url = {https://doi.org/10.1145/3583780.3615158},doi = {10.1145/3583780.3615158},abstract = {Node classification is an important task in many fields, e.g., predicting entity types in knowledge graphs, classifying papers in citation graphs, or classifying nodes in social networks. In many cases, it is crucial to explain why certain predictions are made. Towards this end, concept learning has been proposed as a means of interpretable node classification: given positive and negative examples in a knowledge base, concepts in description logics are learned that serve as classification models. However, state-of-the-art concept learners, including EvoLearner and CELOE exhibit long runtimes. In this paper, we propose to accelerate concept learning with graph sampling techniques. We experiment with seven techniques and tailor them to the setting of concept learning. In our experiments, we achieve a reduction in training size by over 90\% while maintaining a high predictive performance.},booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},pages = {3733–3737},numpages = {5},keywords = {knowledge bases, concept learning, graph sampling},location = {Birmingham, United Kingdom},series = {CIKM '23}}