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
In agent mediated electronic commerce the diversity of the involved actors can lead to diff erent conceptualizations of their needs and capabilities giving rise to semantic incompatibilities that might hamper negotiations and the fulfilling of satisfactory transactions. In order to provide help in the conversation among diff erent agents, these systems should provide ontology services, more specifically, ontology matching services. However, given the natural ambiguity of the ontology matching process, raising the possibility of multiple alignments between the same pair of ontologies, it is necessary to choose the one that best meets the interests of both agents. On the other hand, agents may possess diff erent interests, therefore the ontology alignment may also become the object of further negotiation. In this context, the application and exploitation of relationships captured in social networks can result in the establishment of more accurate adequacy relations of ontology alignments to agents, as well as the improvement of the negotiations' efficiency and, consequently, the users' satisfaction with the electronic commerce system. In this paper we present the AEMOS system which follows an ontology-based information integration approach, exploiting the ontology matching paradigm, improved by the application and exploitation of the relationships captured in the social networks. Copyright © 2014, Australian Computer Society Inc.

Abstract
With the growing popularity of micro-task crowdsourcing platforms, new workflow-based micro-task crowdsourcing approaches are starting to emerge. Such workflows occur in legal, political and conflict resolution domains as well, presenting new challenges, namely in micro-task specification and human-machine interaction, which result mostly from the flow of unstructured data. Domain ontologies provide the structure and semantics required to describe the data flowing throughout the workflow in a way understandable to both humans and machines. This paper presents a method for the construction of micro-task workflows from legal domain ontologies. The method is currently being employed in the context of the UMCourt project in order to formulate information retrieval and conflict resolution workflows.

Abstract
This work presents a recommender system of economic news articles. Its objectives are threefold: (i) managing the vocabulary of the economic news domain to improve the system based on the seamlessly intervention of the documentalist (ii) automatically multi-classify the economic new articles and users profiles based on the domain vocabulary, and (iii) recommend the articles by comparing the multiclassification of the articles and profiles of the users. While several solutions exist to recommend news, multi-classify document and compare representations of items and profiles. They are not automatically adaptable to provide a mutual answer to previous points. Even more, existing approaches lacks substantial correlation with the human and in particular with the documentalist perspective. © Springer International Publishing Switzerland 2015.

Abstract
Determining valuable data among large volumes of data is one of the main challenges in Big Data. We aim to extract knowledge from these sources using a Hierarchical Multi-Label Classification process called Semantic HMC. This process automatically learns a label hierarchy and classifies items from very large data sources. Five steps compose the Semantic HMC process: Indexation, Vectorization, Hierarchization, Resolution and Realization. The first three steps construct automatically the label hierarchy from data sources. The last two steps classify new items according to the label hierarchy. This paper focuses in the last two steps and presents a new highly scalable process to classify items from huge sets of unstructured text by using ontologies and rule-based reasoning. The process is implemented in a scalable and distributed platform to process Big Data and some results are discussed. © 2016 ACM.

Abstract
For the last decade, uebe.Q is being adopted by companies in different business areas and countries for managing compliance with solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive, solid and valuable business logic. When it is deployed for/on a company, it usually demands an extensive and specific adaptation (i.e. software refinement) and configuration process involving DigitalWind's ISO 9000 and ISO 1400 experts as well as software development and operation teams. However, a recent business model change imposed that the evolution and configuration of the software, shifts from DigitalWind (and especially from the development team) to external consultants and to other business partners, along with the fact that different third-party's systems and respective data/information need to be integrated with minimal intervention of the development team. This paper presents and overview of the re-engineering process taken to handle this business model change by adopting (i) ontologies for the specification of business concepts, (ii) closed-world assumption (CWA) rules for the specification of the dynamics of the system and (iii) Domain Specific Language (DSL) for the configuration of the system by domain/business experts. The DSL approach is further described in detail. © 2018 IEEE.