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