Project teaming: Knowledge-intensive design for composing team members

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.