KRAFT -- A Knowledge-Graph-Based Resource Allocation Framework
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915216525099008 |
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| author | Bein, Leon Martin, Niels Pufahl, Luise |
| author_facet | Bein, Leon Martin, Niels Pufahl, Luise |
| contents | Resource allocation in business process management involves assigning resources to open tasks while considering factors such as individual roles, aptitudes, case-specific characteristics, and regulatory constraints. Current information systems for resource allocation often require extensive manual effort to specify and maintain allocation rules, making them rigid and challenging to adapt. In contrast, fully automated approaches provide limited explainability, making it difficult to understand and justify allocation decisions. Knowledge graphs, which represent real-world entities and their relationships, offer a promising solution by capturing complex dependencies and enabling dynamic, context-aware resource allocation. This paper introduces KRAFT, a novel approach that leverages knowledge graphs and reasoning techniques to support resource allocation decisions. We demonstrate that integrating knowledge graphs into resource allocation software allows for adaptable and transparent decision-making based on an evolving knowledge base. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KRAFT -- A Knowledge-Graph-Based Resource Allocation Framework Bein, Leon Martin, Niels Pufahl, Luise Software Engineering Resource allocation in business process management involves assigning resources to open tasks while considering factors such as individual roles, aptitudes, case-specific characteristics, and regulatory constraints. Current information systems for resource allocation often require extensive manual effort to specify and maintain allocation rules, making them rigid and challenging to adapt. In contrast, fully automated approaches provide limited explainability, making it difficult to understand and justify allocation decisions. Knowledge graphs, which represent real-world entities and their relationships, offer a promising solution by capturing complex dependencies and enabling dynamic, context-aware resource allocation. This paper introduces KRAFT, a novel approach that leverages knowledge graphs and reasoning techniques to support resource allocation decisions. We demonstrate that integrating knowledge graphs into resource allocation software allows for adaptable and transparent decision-making based on an evolving knowledge base. |
| title | KRAFT -- A Knowledge-Graph-Based Resource Allocation Framework |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.21636 |