Chi, YL Chen, CY Chi, Yu-Liang Chen, Chung-Yang Project teaming: Knowledge-intensive design for composing team members EXPERT SYSTEMS WITH APPLICATIONS Enterprises frequently use project teams to perform various tasks. In a human-centered, highly collaborative environment, the importance of teamwork exceeds that of individual skill. Appropriate team composition is crucial to the success of ad-hoc teamwork, yet optimizing team composition is challenging. This study utilizes knowledge-intensive approaches to build project teaming models into ontologies. Furthermore, it helps develop a set of logic rules for identifying semantic relationships between individuals. By following a knowledge-base creation process, the factual data of project, workers, and teaming factors can be inserted into ontologies. Based on knowledge inference, reliable knowledge bases are established for selecting project team members in runtime. A case study is presented to demonstrate the effectiveness of the proposed design. Experimental lessons demonstrate that combining rules with ontological knowledge bases not only serves team composition needs, but also achieves knowledge base durability and system reliability. (C) 2008 Elsevier Ltd. All rights reserved. 0957-4174 36 5 9479 9487 10.1016/j.eswa.2008.12.015 ISI:000264782800081 Kim, HJ Kim, W Lee, M Kim, Hak-Jin Kim, Wooju Lee, Myungjin Semantic Web Constraint Language and its application to an intelligent shopping agent DECISION SUPPORT SYSTEMS Semantic Web society was initially focused only on data, but then gradually moved toward knowledge. If a vision of the Semantic Web is to enhance humans' decision-making assisted by machines, a missing but important part is knowledge about constraints on data and concepts represented by ontology. This paper proposes a Semantic Web Constraint Language (SWCL) based on OWL, and shows its effectiveness in representing and solving an internet shopper's decision-making problems by implementing a shopping agent in the Semantic Web environment. (C) 2008 Elsevier B.V. All rights reserved. 0167-9236 46 4 Sp. Iss. SI 882 894 10.1016/j.dss.2008.12.004 ISI:000264701000013 Boticario, JG Santos, OC Boticario, Jesus G. Santos, Olga C. A Standards-based Modelling Approach for Dynamic Generation of Adaptive Learning Scenarios JOURNAL OF UNIVERSAL COMPUTER SCIENCE 12th International Conference on Artificial Intelligence in Education (AI-Ed 2005) 2005 Amsterdam, NETHERLANDS One of the key problems in developing standard based adaptive courses is the complexity involved in the design phase, especially when establishing the hooks for the dynamic modelling to be performed at runtime. This is particularly critical when the courses are based on adaptation-oriented learning scenarios, where the full eLearning cycle (design, publication, use and auditing) is considered. Based on the problems we experienced in developing such scenarios with a reusable, platform independent, objective-based approach in the aLFanet project we have established an alternative framework in the ADAPTAPlan project, which focuses on dynamically generating learning design templates with the support of user modelling, planning and machine learning techniques. In particular, in this paper we describe the problems we are tackling and how we are relaxing the design work by automatically building the IMS learning design of the course from a simplified set of data required from the course authors. 0948-695X 14 17 2859 2876 ISI:000264754400007>