An Ontology-Based Tool for Collaborative and Social Sensemaking

• Ana Cristina Bicharra Garcia UFF
• Fernando Pinto ADDLabs/UFF
• Nayat Sanchez-Pi ADDLabs/UFF

Resumo

Sensemaking activities of social networks involve network exploration and representation so, visual tools are designed to support these two activities. Existing social network analysis tools are usually weak in supporting complex analytical tasks and also in providing a collaborative environment for interaction. The analysis of data using a visual tool is rarely a task done in isolation, it tends to be part of a wider goal: that of making sense of the current situation, often to support decision-making. This paper discusses the storytelling design of a software environment to support organizations in sense-making activities and to support accidents investigation. A case study ACR-C describes petroleum industry employees investigating the root cause of an accident issue observed in one (or more) platforms. It is used throughout the paper as an example of human computer interaction where the ontology becomes a tool with domain knowledge to assist expert persons building a root cause tree leading to accidents. The framework will also provide with a collaborative recommendation module assuming that the users build up clusters based on their similar analysis in rating of items. A case study ACR-C describes petroleum industry employees investigating the root cause of an accident issue observed in one (or more) platforms. It is used throughout the paper as an example of human computer interaction where the ontology becomes a tool with domain knowledge to assist expert persons buildind a root cause tree leading to accidents. This paper reports the experience gained in ACR-C, a project that aims to support knowledge management (KM), sharing and reuse across different media in oil and gas industry. We report the storytelling design approach adopted and the design phases that led to the First prototype. A user interface was designed to assess how different levels of data, information and knowledge were mapped using alternative visual tools. The results show that a clear separation of the visual data analysis from other sense-making subtasks helps users in focussing their attention and comprehension of root causes of the problem. Further work is needed to develop more fully intuitive visualizations that exploit the richer information and make the multiple connections between data more easily accessible.

Palavras-chave: Collaborative analysis recommendation, Social network, Root cause analysis, ontology