Implementing Knowledge Representation and Reasoning with Object Oriented Design

2026-01-21

AI Summary

Existing knowledge representation and reasoning (KR&R) systems struggle to integrate effectively with object-oriented programming (OOP), limiting their applicability in complex software systems. This work proposes KRROOD, a novel framework that, for the first time, embeds knowledge as a first-class programming abstraction directly within OOP class structures. By leveraging native object-oriented mechanisms, KRROOD enables seamless knowledge representation and reasoning, thereby bridging the paradigmatic gap between logic-based programming and OOP. The approach supports expressive ontology modeling and reasoning, and its efficacy is empirically validated through evaluations on the OWL2Bench benchmark and human-robot collaborative task-learning experiments. Results demonstrate significant improvements in both the reasoning capabilities of autonomous systems in real-world scenarios and their engineering integrability into mainstream software architectures.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Statistical Relational/Logic Learning

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper introduces KRROOD, a framework designed to bridge the integration gap between modern software engineering and Knowledge Representation&Reasoning (KR&R) systems. While Object-Oriented Programming (OOP) is the standard for developing complex applications, existing KR&R frameworks often rely on external ontologies and specialized languages that are difficult to integrate with imperative code. KRROOD addresses this by treating knowledge as a first-class programming abstraction using native class structures, bridging the gap between the logic programming and OOP paradigms. We evaluate the system on the OWL2Bench benchmark and a human-robot task learning scenario. Experimental results show that KRROOD achieves strong performance while supporting the expressive reasoning required for real-world autonomous systems.

Problem

• Knowledge Representation and Reasoning
• Object-Oriented Programming
• Software Integration
• Ontologies
• Imperative Code

Innovation

• Knowledge Representation and Reasoning
• Object-Oriented Programming
• First-class Abstraction
• KRROOD
• Ontology Integration