An Integrated Approach for Automatic Aggregation of Learning Knowledge Objects

This paper presents the Knowledge Puzzle, an ontology-based platform designed to facilitate domain knowledge acquisition from textual documents for knowledge-based systems. First, the Knowledge Puzzle Platform performs an automatic generation of a domain ontology from documents’ content through natural language processing and machine learning technologies. Second, it employs a new content model, the Knowledge Puzzle Content Model, which aims to model learning material from annotated content. Annotations are performed semi-automatically based on IBM’s Unstructured Information Management Architecture and are stored in an Organizational memory (OM) as knowledge fragments. The organizational memory is used as a knowledge base for a training environment (an Intelligent Tutoring System or an e-Learning environment). The main objective of these annotations is to enable the automatic aggregation of Learning Knowledge Objects (LKOs) guided by instructional strategies, which are provided through SWRL rules. Finally, a methodology is proposed to generate SCORM-compliant learning objects from these LKOs.