Context: For thousands of years humans have been using natural language to register their knowledge on important information to enable its access to future generations. With internet, a large amount of textual data is produced and shared on a daily basis. So, scientists started to research techniques for efficiently process knowledge stored in textual format. In this context, Natural Language Processing (NLP) became a popular area studying linguistic phenomena and using computational methods to process texts in natural language. In particular, Open Information Extraction (Open IE) was proposed to gather information from plain text. Despite the advances in this area, it is still necessary to map details about how these approaches were proposed to support the community while creating more efficient Open IE systems. Objective: In this paper, we identify, in the literature, the main characteristics of proposed Open IE approaches. Method: First, we extended the search performed in a systematic mapping previously published by using backward snowballing and a manual search. Next, we updated the electronic database search including ACL Anthology. Finally, 159 studies proposing Open IE approaches were considered for data extraction. Results: Data analysis showed a significant increase in the number of studies published about Open IE in the last years. In addition, we provide important details about how these techniques were proposed (e.g., data sets used and output evaluation techniques). Results indicate that researchers started to adopt neural networks to perform Open IE instead of using conventional supervised learning techniques. Conclusion: Recent advances in Artificial Intelligence and neural networks techniques allowed scientists to have a new perspective on how to perform efficient textual data management. Therefore, Open IE approaches gained much attention as they can help in many contexts, especially in knowledge management tasks.

Collaborative decision making (CDM) has been used as an essential paradigm to increase the efficiency of air traffic flow management (ATFM), including takeoff or landing operations at airports. Air traffic control (ATC) services and airlines have been involved in the current CDM, but airport management service, an important stakeholder, has not been involved yet, generally. This paper proposes a new CDM model, which is named satisficing CDM, that is based on the satisficing game theory. This model includes three main entities (ATC, airlines, and airport management) in ATFM. The complete set of functions (preference, rejectability, and selectability) is established for each of the entities. Because the delay due to ground or air holding potentially alters the takeoff or the landing order of a flight, the sequence of takeoff and landing is determined through the satisficing negotiation process. To demonstrate the utility of the developed intelligent system, experiments are run with real air traffic in the terminal area of Sao Paulo. The experimental results show the importance and effectiveness of including airport management services in CDM. The sequences of takeoff and landing determined by the proposed model mostly meet the preferences of the three stakeholders in the given real traffic scenarios.