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.

Manipulation-relevantcommon-sense knowledge is crucial to support actionplanning for complex tasks.In particular, instrumentality information of what can be done with certain tools can be used to limit the search space which is growing exponentially with the number of viable options.Typical sources for such knowledge, structured common-sense knowledge bases such as ConceptNet or WebChild, provide a limited amount of information which also varies drastically across different domains.Considering the recent success of pre-trained language models such as BERT, we investigate whether common-sense information can directly be extracted from semi-structured text with an acceptable annotation effort.Concretely, we compare the common-sense relations obtained from ConceptNet versus those extracted with BERT from large recipe databases.In this context, we propose a scoring function, based on the WordNet taxonomy to match specific terms to more general ones, enabling a rich evaluation against a set of ground-truth relations.

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.