A Novel Knowledge Modeling Framework POTMe and Its Application for Col-laborative Problem Solving

Abstract

Abstract. Collaborative efforts are inevitably demanded when solving a complex problem, which is often plagued with incomplete, ill-structured, and poor quality information. Therefore, a global knowledge modeling framework is required for cross-domain knowledge coordination. However, current knowledge modeling techniques pay little attention to temporal knowledge that is indispensable for navigating cross-domain collaborative task executions and their collaboration. In view of this challenge, a novel knowledge modeling framework, named POTMe, is investigated in this paper. This knowledge modeling framework provides a structured application context by articulating knowledge dependencies and temporal dependencies among problemsolving activities. The main contribution of this paper is twofold. First, this novel knowledge modeling framework provides a structured problem solving context. It enhances the consistency of global knowledge coordination among collaborative problem-solving activities. Second, temporal knowledge modeling is incorporated into this knowledge modeling framework. It is reified by a temporal reasoning rule, and aims at evaluating cross-domain task collaboration in an incorporated executive environment. [3] believed that a human-engineered activity could be characterized by three traditional engineering activities, i.e., 1) stakeholders define their needs and requirements, 2) engineers design the product based on stakeholders' definitions, and 3) producers build the product based on engineers' designs. These activities are often accompanied by cross-domain knowledge sharing and collaboration. The first and second activities could be characterized by product conceiving behaviors. The third activity could be featured by product producing behavior based on the product blueprint. IntroductionIn collaborative problem solving, qualitative problem-defining process equal to a group of product conceiving behaviors, while quantitative parameter computing and verifying process could be treated as a group of product producing behaviors. Generally, they are two different application stages. The first stage mainly concentrates on preliminary knowledge discovery with qualitative problem cognition . In practice, a complex problem and its collaboration solving are often initiated by incomplete, ill structured and poor quality information. It is always a challenging endeavor