KRAFT -- A Knowledge-Graph-Based Resource Allocation Framework

2025-03-27

AI Summary

To address the rigidity and poor interpretability of resource allocation systems in business process management, this paper proposes a dynamic, explainable resource scheduling method integrating knowledge graphs with logical reasoning. We construct an evolvable knowledge graph incorporating roles, competencies, case features, and compliance constraints, and employ OWL ontology modeling, SPARQL querying, SWRL rule-based inference, and context-aware matching algorithms to enable flexible rule evolution and fully traceable decision-making. This work is the first to embed an evolvable knowledge graph into resource allocation systems—preserving the transparency of rule engines while overcoming the opacity inherent in black-box AI approaches. Empirical evaluation on real-world datasets demonstrates a 19% improvement in allocation accuracy, a 73% reduction in rule maintenance cost, and full decision traceability to underlying graph entities and relations.

Technology Category

• Knowledge Representation and Reasoning: Description Logics
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Automating resource allocation in business processes with knowledge graphs
• Reducing manual effort and rigidity in current allocation systems
• Enhancing explainability and adaptability of allocation decisions

Innovation

• Leverages knowledge graphs for resource allocation
• Enables dynamic context-aware decision-making
• Integrates reasoning techniques for transparency