An Integrated Approach for Automatic\ud Aggregation of Learning Knowledge Objects

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

Intelligent tutoring systems for systems engineering methodologies

The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

TREE-D-SEEK: A Framework for Retrieving Three-Dimensional Scenes

In this dissertation, a strategy and framework for retrieving 3D scenes is proposed. The strategy is to retrieve 3D scenes based on a unified approach for indexing content from disparate information sources and information levels. The TREE-D-SEEK framework implements the proposed strategy for retrieving 3D scenes and is capable of indexing content from a variety of corpora at distinct information levels. A semantic annotation model for indexing 3D scenes in the TREE-D-SEEK framework is also proposed. The semantic annotation model is based on an ontology for rapid prototyping of 3D virtual worlds. With ongoing improvements in computer hardware and 3D technology, the cost associated with the acquisition, production and deployment of 3D scenes is decreasing. As a consequence, there is a need for efficient 3D retrieval systems for the increasing number of 3D scenes in corpora. An efficient 3D retrieval system provides several benefits such as enhanced sharing and reuse of 3D scenes and 3D content. Existing 3D retrieval systems are closed systems and provide search solutions based on a predefined set of indexing and matching algorithms Existing 3D search systems and search solutions cannot be customized for specific requirements, type of information source and information level. In this research, TREE-D-SEEK—an open, extensible framework for retrieving 3D scenes—is proposed. The TREE-D-SEEK framework is capable of retrieving 3D scenes based on indexing low level content to high-level semantic metadata. The TREE-D-SEEK framework is discussed from a software architecture perspective. The architecture is based on a common process flow derived from indexing disparate information sources. Several indexing and matching algorithms are implemented. Experiments are conducted to evaluate the usability and performance of the framework. Retrieval performance of the framework is evaluated using benchmarks and manually collected corpora. A generic, semantic annotation model is proposed for indexing a 3D scene. The primary objective of using the semantic annotation model in the TREE-D-SEEK framework is to improve retrieval relevance and to support richer queries within a 3D scene. The semantic annotation model is driven by an ontology. The ontology is derived from a 3D rapid prototyping framework. The TREE-D-SEEK framework supports querying by example, keyword based and semantic annotation based query types for retrieving 3D scenes

Knowledge Extraction and Summarization for Textual Case-Based Reasoning: A Probabilistic Task Content Modeling Approach

Case-Based Reasoning (CBR) is an Artificial Intelligence (AI) technique that has been successfully used for building knowledge systems for tasks/domains where different knowledge sources are easily available, particularly in the form of problem solving situations, known as cases. Cases generally display a clear distinction between different components of problem solving, for instance, components of the problem description and of the problem solution. Thus, an existing and explicit structure of cases is presumed. However, when problem solving experiences are stored in the form of textual narratives (in natural language), there is no explicit case structure, so that CBR cannot be applied directly. This thesis presents a novel approach for authoring cases from episodic textual narratives and organizing these cases in a case base structure that permits a better support for user goals. The approach is based on the following fundamental ideas: - CBR as a problem solving technique is goal-oriented and goals are realized by means of task strategies. - Tasks have an internal structure that can be represented in terms of participating events and event components. - Episodic textual narratives are not random containers of domain concept terms. Rather, the text can be considered as generated by the underlying task structure whose content they describe. The presented case base authoring process combines task knowledge with Natural Language Processing (NLP) techniques to perform the needed knowledge extraction and summarization

FLOOR (Framework for Linking Ontology Objects and Textual Requirements): A New Requirements Engineering Tool that Provides Real-time Feedback

Cost overruns on complex system-of-systems development programs frequently trace back to problems with requirements. For increasingly complex systems, a key capability is the identification and management of requirements early in a system’s life cycle, when errors are cheapest and easiest to correct. Significant work has been done to apply natural language processing (NLP) to the domain of requirements engineering. Recently, requirements engineering tools have been developed that use NLP to leverage both domain ontologies and requirement templates, which define acceptable sentence structures for requirements. Domain ontologies provide terminology consistency, and enable rule-checking during the testing of requirements. This thesis introduces FLOOR, a new software tool for requirements engineering that leverages NLP. FLOOR not only integrates domain ontologies and requirement templates, but also supports importing multiple external domain ontologies