CQD-SHAP: Explainable Complex Query Answering via Shapley Values

1. 1Implement CQD-SHAP using Python and the arXiv library
2. 2Run experiments to evaluate the performance of CQD-SHAP on complex query answering tasks
3. 3Configure the Shapley value calculation to optimize explainability
4. 4Test the robustness of CQD-SHAP on incomplete knowledge graphs
5. 5Apply CQD-SHAP to real-world complex query answering tasks

Data scientists and AI engineers on a team can benefit from CQD-SHAP to improve the explainability of their complex query answering models, increasing user trust and adoption

💡 CQD-SHAP increases user trust in AI models by providing explainable complex query answering

🤖 CQD-SHAP explains complex query answering via Shapley values! 📈