Autilia Vitiello

A hybrid evolutionary approach for solving the ontology alignment problem

Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so‐called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high‐quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms.

Distributing emotional services in Ambient Intelligence through cognitive agents

Ambient Intelligence (AmI) is a pervasive computing paradigm whose main aim is to design smart environments composed of invisible, connected, intelligent and interactive systems, which are naturally sensitive and responsive to the presence of people, providing advanced services for improving the quality of life. Nevertheless, AmI systems are more than a simple integration among computer technologies; indeed, their design can strongly depend upon psychology and social sciences aspects describing, analysing and forecasting the human being status during the system’s decision making. This paper introduces a novel methodology for AmI systems designing that exploits a service-oriented architecture whose functionalities are performed by a collection of so-called cognitive agents. These agents exploit a novel extension of Fuzzy Cognitive Maps benefiting on the theory of Timed Automata and a formal method for representing human moods in order to distribute emotional services able to enhance users’ comfort and simplify the human/systems interactions. As will be shown in experimental results, where a usability study and a confirmation of expectations test have been performed, the proposed approach maximizes the system’s usability in terms of efficiency, accuracy and emotional response.

A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of systems concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models.

Improving ontology alignment through memetic algorithms

Born primarily as means to model knowledge, ontologies have successfully been exploited to enable knowledge exchange among people, organizations and software agents. However, because of strong subjectivity of ontology modeling, a matching process is necessary in order to lead ontologies into mutual agreement and obtain the relative alignment, i.e., the set of correspondences among them. The aim of this paper is to propose a memetic algorithm to perform an automatic matching process capable of computing a suboptimal alignment between two ontologies. To achieve this aim, the ontology alignment problem has been formulated as a minimum optimization problem characterized by an objective function depending on a fuzzy similarity. As shown in the performed experiments, the memetic approach results more suitable for ontology alignment problem than other evolutionary techniques such as genetic algorithms.

Hybridizing fuzzy control and timed automata for modeling variable structure fuzzy systems

During the past several years, fuzzy control has emerged as one of the most suitable and efficient methods for designing and developing complex systems in environments characterized by high level of uncertainty and imprecision. Nowadays, this methodology is used to model systems in several applications domains which range from industrial machineries to financial decisions support systems. Nevertheless, in spite of the usefulness of fuzzy control, one of its drawbacks comes from the lack of the temporal concept that is crucial in many systems characterized from a discontinuous nonlinear behaviour. In particular, in its standard vision, fuzzy control is not able to represent Variable-Structure systems, i.e., systems that change their configuration (knowledge base) or their behaviour (rule base) over time. To overcome these drawbacks, this paper extends fuzzy control idea by considering a theory from formal languages: timed automata. This novel synergic approach achieves a twofold advantage by representing a system in qualitative and linguistic way and introducing a novel switching control concept able to maximize systems performances and robustness.

Exploiting Timed Automata based Fuzzy Controllers for voltage regulation in Smart Grids

The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, the optimal coordination of distributed voltage controllers is one of the main issues to address. In this field, the application of traditional control paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGSs) require more scalable, more flexible control and regulation paradigms. To try and overcome these challenges, this paper proposes the concept of a decentralized non-hierarchical voltage regulation architecture based on intelligent and cooperative smart entities. The distributed voltage controllers employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables that characterize the operation of the global Smart Grid. These variables are then amalgamated by a novel fuzzy inference engine, named Timed Automata based Fuzzy Controllers, in order to identify proper control actions aimed at improving the grid voltage profile and reducing power losses.

Enhancing Transparent Fuzzy Controllers Through Temporal Concepts: An Application to Computer Games

In the last years, FML (Fuzzy Markup Language) is emerging as one of the most efficient and useful language to define a fuzzy control thanks to its capability of modeling Fuzzy Logic Controllers in a human-readable and hardware independent way, i.e. the so-called Transparent Fuzzy Controllers (TFCs). However, although a FML fuzzy control is suitable to be employed in a wide range of applications, a Transparent Fuzzy Controller has a remarkable drawback: it lacks of the temporal concept. Time is crucial component in many systems above all if characterized from a discontinuous nonlinear behaviour as Variable-Structure Fuzzy systems, i.e., systems that change their configuration (knowledge base) or their behaviour (rule base) over time. To overcome TFCs temporal drawback, this paper combines FML fuzzy control together with a theory from formal languages: timed automata. The proposed synergic approach, called Timed Automata based Fuzzy Control, achieves a twofold advantage by representing a system in qualitative and hardware independent way and introducing a novel switching control concept able to maximize system’s performances as will be shown by means of an application in video games context.

A comparison of multi-objective evolutionary algorithms for the ontology meta-matching problem

In recent years, several ontology-based systems have been developed for data integration purposes. The principal task of these systems is to accomplish an ontology alignment process capable of matching two ontologies used for modeling heterogeneous data sources. Unfortunately, in order to perform an efficient ontology alignment, it is necessary to address a nested issue known as ontology meta-matching problem consisting in appropriately setting some regulating parameters. Over years, evolutionary algorithms are appeared to be the most suitable methodology to address this problem. However, almost all of existing approaches work with a single function to be optimized even though a possible solution for the ontology meta-matching problem can be viewed as a compromise among different objectives. Therefore, approaches based on multi-objective optimization are emerging as techniques more efficient than conventional evolutionary algorithms in solving the meta-matching problem. The aim of this paper is to perform a systematic comparison among well-known multi-objective Evolutionary Algorithms (EAs) in order to study their effects in solving the meta-matching problem. As shown through computational experiments, among the compared multi-objective EAs, OMOPSO statistically provides the best performance in terms of the well-known measures such as hypervolume, Δ index and coverage of two sets.

Hybridizing genetic algorithms and hill climbing for similarity aggregation in ontology matching

Ontology Matching aims at finding correspondences between two different ontologies with overlapping parts in order to bring them into a mutual agreement. The set of correspondences, called alignment, is obtained by computing an aggregated similarity value for all pairs of ontology entities through a weighted approach. Unfortunately, the similarity aggregation task is a very complex optimization process, above all, when no information is known about ontology characteristics. This work presents a hybrid approach which aims at efficiently optimizing the weights for the similarity aggregation task without knowing a priori the ontology features. The effectiveness of our approach is shown by aligning ontologies belonging to the well-known OAEI benchmark dataset and by executing a comparison based on the Wilcoxons signed rank test which highlights that our proposal statistically outperforms both its genetic counterpart and a traditional no evolutionary approach.

Solving the shopping plan problem through bio-inspired approaches

Blended commerce involves all commerce experiences in which customers make use of different channels (online, offline and mobile) for their purchases to take advantages with respect to their needs and attitudes. This new e-commerce trend is typically characterized by so-called loyalty programmes such as coupons and system points. These mechanisms can be extremely useful for the companies to achieve customer retention and for the customers to obtain discounts. However, loyalty programmes can complicate for customers the evaluation of all offers and the selection of optimal providers (shopping plan) for buying the desired set of products. To face this problem, referred as Shopping Plan Problem, optimization algorithms are emerging as a suitable methodology. This paper is aimed at performing a systematic comparison amongst three bio-inspired optimization approaches, genetic algorithms, memetic ones and ant colony optimization, to detect the best performer for solving the shopping plan problem in a blended shopping scenario.