Kenneweg S, Deigmöller J, Cimiano P, Eggert J. Benchmarking the Ability of Large Language Models to Reason About Event Sets. In: Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SCITEPRESS - Science and Technology Publications; 2024: 74-82.

In this paper, we introduce Memory Nets, a knowledge representation targeted at Autonomous Intelligent Agents (IAs) operating in real world. The main focus is on a knowledge base (KB) that on the one hand is able to leverage the large body of openly available semantic information, and on the other hand allows to incrementally accumulate additional knowledge from situated interaction. Such a KB can only rely on operable semantics fully contained in the knowledge base itself, avoiding any type of hidden semantics in the KB attributes, such as human-interpretable identifier. In addition, it has to provide means for tightly coupling the internal representation to real-world events. We propose a KB structure and inference processes based on a knowledge graph that has a small number of link types with operational semantics only, and where the main information lies in the complex patterns and connectivity structures that can be build incrementally using these links. We describe the basic domain independent features of Memory Nets and the relation to measurements and actuator capabilities as available by autonomous entities, with the target of providing a KB framework for researching how to create IAs that continuously expand their knowledge about the world.