Generating Chunks for Cognitive Architectures

Abstract

Knowledge engineering is an important task for creating and maintaining a knowledge base for cognitive models. It involves acquiring, representing, and organizing knowledge in a form that computers can use to make decisions and solve problems. However, this process can be a bottleneck for designing and using cognitive models. Knowledge engineering is a time-consuming and resource-intensive task that requires subject matter experts to provide information about a domain. In addition, models can acquire knowledge but require significant mechanisms to structure that information in a structured format appropriate for general use. Given the knowledge engineering bottleneck, we propose a solution that relies on natural language processing to extract key entities, relationships, and attributes to automatically generate chunks encoded as triples or chunks from unstructured text. Once generated, the knowledge can be used to create or add to a knowledge base within cognitive architectures to reduce knowledge engineering and task-specific models.