Browsing by Author "Cardinale, Y."

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    Context-aware and ontology-based recommender system for e-tourism
    (SciTePress, 2021) Castellanos, G.; Cardinale, Y.; Roose, P.
    Frequently, travelers try to collect information for planing a trip or when being at the destination. Usually, tourists depend on places’ reviews to make the choice, but this implies prior knowledge of the touristic places and explicit search for suggestions through interaction with applications (i.e., PULL paradigm). In contrast, a PUSH approach, in which the application proactively triggers a recommendation process according to users’ preferences and when necessary, seems to be a more reasonable solution. Recommender systems have become appropriate applications to help tourists in their trip planning. However, they still have limitations, such as poor consideration of users’ profiles and their contexts, their predictable suggestions, and the lack of a standard representation of the knowledge managed. We propose a user-centric recommender system architecture, that supports both PULL and PUSH approaches, assisted by an ontology-based spreading activation algorithm for context-aware recommendations, with a focus on decreasing predictable outputs and increasing serendipity, based on an aging-like approach. To demonstrate its suitability and performance, we develop a first prototype of the architecture and simulate different scenarios, varying users’ profiles, preferences, and context parameters. Results show that the ontology-based spreading activation and the proposed aging system provide relevant and varied recommendations according to users’ preferences, while considering their context and improving the serendipity of the system when comparing with a state-of-the-art work
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    Ontoslam: An ontology for representing location and simultaneous mapping information for autonomous robots
    (MDPI, 2021) Cornejo-Lupa, M.A.; Cardinale, Y.; Ticona-Herrera, R.; Barrios Aranibar, D.; Andrade, M.; Diaz-Amado, J.
    "Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interopera-ble solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics. © 2021 by the authors. Licensee MDPI, Basel, Switzerland"