Abstract
Analysis of automatically-generated alignments shows that ambiguous situations are quite common, preventing their application in scenarios demanding high quality and completeness, such ontology mediation (e.g. data transformation and information/knowledge integration). Even the best-performing alignment needs to be manually corrected, completed and verified before application. In this paper, we propose a decision support system (DSS) based in a general-purpose rule engine that assists the expert on improving and completing the automatically-generated alignments into fully-fledged alignments, balancing the precision and recall of the system with the user participation in the process. For that, the rules capture the preconditions (existing facts) and the actions to solve each (ambiguous) alignment scenario, in which the expert decision will be adopted in further automatic decisions. The evaluation of the proposed DSS shows the gain in reducing the need for expert's decisions while increasing the accuracy of the alignments.

Abstract
Ontology matchers establish correspondences between ontologies to enable knowledge from different sources and domains to be used in ontology mediation tasks (e.g. data transformation and information/ knowledge integration) in many ways. While these processes demand great quality alignments, even the best-performing alignment needs to be corrected and completed before application. In this paper, we propose a rule-based system that improves and completes the automatically-generated alignments into fullyfledged alignments. For that, the rules capture the pre-conditions (existing facts) and the actions to solve each (ambiguous) scenario, in which automatic decisions supported by a folksonomy-based matcher are adopted. The evaluation of the proposed system shows the increasing accuracy of the alignments.

Abstract
The work described in this paper is part of the development of a framework to support the joint execution of cooperative missions by a group of vehicles, in a simulated, augmented, or real environment. Such a framework brings forward the need for formal languages in which to specify the vehicles that compose a team, the scenario in which they will operate, and the mission to be performed. This paper introduces the Scenario Description Language (SDL) and the Team Description Language (TDL), two Extensible Markup Language based dialects that compose the static components necessary for representing scenario and mission knowledge. SDL provides a specification of physical scenario and global operational constraints, while TDL defines the team of vehicles, as well as team-specific operational restrictions. The dialects were defined using Extensible Markup Language schemas, with all required information being integrated in the definitions. An interface was developed and incorporated into the framework, allowing for the creation and edition of SDL and TDL files. Once the information is specified, it can be used in the framework, thus facilitating environment and team specification and deployment. A survey answered by practitioners and researchers shows that the satisfaction with SDL+TDL is elevated (the overall evaluation of SDL+TDL achieved a score of 4 out of 5, with 81%/78.6% of the answers 4); in addition, the usability of the interface was evaluated, achieving a score of 86.7 in the System Usability Scale survey. These results imply that SDL+TDL is flexible enough to represent scenarios and teams, through a user-friendly interface.

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