Knowledge-Based News Event Analysis and Forecasting Toolkit
Abstract
We present a toolkit for knowledge-based news event analysis and forecasting. The toolkit is powered by a Knowledge Graph (KG) of events curated from structured and unstructured sources of event-related knowledge. The toolkit provides functions for 1) mapping ongoing news headlines to concepts in the KG, 2) retrieval, reasoning, and visualization for causal analysis and forecasting, and 3) extraction of causal knowledge from text documents to augment the KG with additional domain knowledge. Each function has a number of implementations using a wide range of state-of-the-art neuro-symbolic techniques. We show how the toolkit enables building a human-in-the-loop explainable solution for event analysis and forecasting.
Cite
Text
Hassanzadeh et al. "Knowledge-Based News Event Analysis and Forecasting Toolkit." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/850Markdown
[Hassanzadeh et al. "Knowledge-Based News Event Analysis and Forecasting Toolkit." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/hassanzadeh2022ijcai-knowledge/) doi:10.24963/IJCAI.2022/850BibTeX
@inproceedings{hassanzadeh2022ijcai-knowledge,
title = {{Knowledge-Based News Event Analysis and Forecasting Toolkit}},
author = {Hassanzadeh, Oktie and Awasthy, Parul and Barker, Ken and Bhardwaj, Onkar and Bhattacharjya, Debarun and Feblowitz, Mark and Martie, Lee and Ni, Jian and Srinivas, Kavitha and Yip, Lucy},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5904-5907},
doi = {10.24963/IJCAI.2022/850},
url = {https://mlanthology.org/ijcai/2022/hassanzadeh2022ijcai-knowledge/}
}