Memetic algorithms for ontology alignment

2011 - 2012Semantic interoperability represents the capability of two or more systems to meaningfully and accurately interpret the exchanged data so as to produce useful results. It is an essential feature of all distributed and open knowledge based systems designed for both e-government and private businesses, since it enables machine interpretation, inferencing and computable logic. Unfortunately, the task of achieving semantic interoperability is very difficult because it requires that the meanings of any data must be specified in an appropriate detail in order to resolve any potential ambiguity. Currently, the best technology recognized for achieving such level of precision in specification of meaning is represented by ontologies. According to the most frequently referenced definition [1], an ontology is an explicit specification of a conceptualization, i.e., the formal specification of the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold them [2]. However, different tasks or different points of view lead ontology designers to produce different conceptualizations of the same domain of interest. This means that the subjectivity of the ontology modeling results in the creation of heterogeneous ontologies characterized by terminological and conceptual discrepancies. Examples of these discrepancies are the use of different words to name the same concept, the use of the same word to name different concepts, the creation of hierarchies for a specific domain region with different levels of detail and so on. The arising so-called semantic heterogeneity problem represents, in turn, an obstacle for achieving semantic interoperability... [edited by author]XI n.s

A comparison of fuzzy approaches for training a humanoid robotic football player

© 2017 IEEE. Fuzzy Systems are an efficient instrument to create efficient and transparent models of the behavior of complex dynamic systems such as autonomous humanoid robots. The human interpretability of these models is particularly significant when it is applied to the cognitive robotics research, in which the models are designed to study the behaviors and produce a better understanding of the underlying processes of the cognitive development. From this research point of view, this paper presents a comparative study on training fuzzy based system to control the autonomous navigation and task execution of a humanoid robot controlled in a soccer scenario. Examples of sensor data are collected via a computer simulation, then we compare the performance of several fuzzy algorithms able to learn and optimize the humanoid robot's actions from the data

An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects

The Internet of Things (IoT) paradigm is establishing itself as a technology to improve data acquisition and information management in the construction field. It is consolidating as an emerging technology in all phases of the life cycle of projects and specifically in the execution phase of a construction project. One of the fundamental tasks in this phase is related to Health and Safety Management since the accident rate in this sector is very high compared to other phases or even sectors. For example, one of the most critical risks is falling objects due to the peculiarities of the construction process. Therefore, the integration of both technology and safety expert knowledge in this task is a key issue including ubiquitous computing, real-time decision capacity and expert knowledge management from risks with imprecise data. Starting from this vision, the goal of this paper is to introduce an IoT infrastructure integrated with JFML, an open-source library for Fuzzy Logic Systems according to the IEEE Std 1855-2016, to support imprecise experts’ decision making in facing the risk of falling objects. The system advises the worker of the risk level of accidents in real-time employing a smart wristband. The proposed IoT infrastructure has been tested in three different scenarios involving habitual working situations and characterized by different levels of falling objects risk. As assessed by an expert panel, the proposed system shows suitable results.This research was funded by University of Naples Federico II through the Finanziamento della Ricerca di Ateneo (FRA) 2020 (CUP: E69C20000380005) and has been partially supported by the ”Programa de ayuda para Estancias Breves en Centros de Investigación de Calidad” of the University of Málaga and the research project BIA2016-79270-P, the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund-ERDF (Fondo Europeo de Desarrollo Regional-FEDER) under project PGC2018-096156-B-I00 Recuperación y Descripción de Imágenes mediante Lenguaje Natural usando Técnicas de Aprendizaje Profundo y Computación Flexible and the Andalusian Government under Grant P18-RT-2248

Automatic Event Geo-Location in Twitter

n/

An Adaptive Neuro-Fuzzy Inference System for the Qualitative Study of Perceptual Prominence in Linguistics

Vitiello A, Acampora G, Cutugno F, Wagner P, Origlia A. An Adaptive Neuro-Fuzzy Inference System for the Qualitative Study of Perceptual Prominence in Linguistics. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017). Piscataway, NJ: IEEE; 2017

The frameshift Leu220Phefs*2 variant in KRIT1 accounts for early acute bleeding in patients affected by cerebral cavernous malformation

Abstract Background and Objectives Cerebral cavernous malformation (CCM) is a neurovascular disease characterized by abnormally expanded and tortuous microvessels with increased predisposition to thrombosis and focal hemorrhage. Its incidence is estimated to range between 0.4% and 0.8%. Sporadic and familial forms of CCM are described. The first one is characterized by single lesion, while the familial form is defined by multiple malformations. In this scenario, more than 300 mutations affecting the CCM genes have been described to date, but the exact pathogenic mechanism is yet unknown. Most of the causative variants of KRIT1 gene are frameshift but there are many missense and nonsense variants and they have been found some splicing mutations. The diagnosis is based on magnetic resonance images (MRI) and genetic testing. Case report A 15-year-old male presented with a two weeks duration worsening headache accompanied by vomiting and three months behavioral changes. Computer tomography revealed a large right temporal lesion with other smaller in left parietal and left cerebellar region. At the time of diagnosis, the two siblings of the proband were asymptomatic. Nevertheless, four months later, the 7-years-old brother was admitted to the emergency room for balance deficit, diplopia, right-hitting nystagmus and stiff neck with deviation of the head. A cerebral CT revealed polylobate hyperdense mass of the middle cerebral pedicle associated to acute bleeding. A genetic testing for hereditary cavernous brain malformation was carried out. Results The molecular analysis identified a 2-bp duplication (NM_194456.1:c.658_659dupTT) as heterozygous within the exon 8 of CCM1/KRIT1 gene (Fig. 1C). This duplication leads to a frameshift variant, resulting in a premature stop codon (p.Leu220Phefs*2). Discussion The clinical data collected confirm the variable phenotypic expression of CCM and suggest a greater severity of symptoms in the youngest patients

Collaborative memetic agents for enabling semantic interoperability

Semantic interoperability represents the ability of two or more systems to automatically interpret the information exchanged meaningfully in order to produce useful results. Currently, the best recognized technology for achieving a specification of meaning is represented by ontologies. However, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with discrepancies and heterogeneities. As a consequence, an ontology alignment process is necessary for bridging this gap and achieving a full communication understanding across different software components. This paper uses a synergetic approach, based on the integration of collaborative agents and parallel memetic algorithms, for efficiently aligning ontologies and, consequently, solving the semantic heterogeneity problem. As shown by a statistical procedure, our approach yields high performance in terms of the ratio between alignment quality and computational effort with respect to conventional evolutionary approaches for ontology alignment. © 2013 IEEE