Saverio Salerno

Ontology Extraction for Knowledge Reuse: The e-Learning Perspective

Ontologies have been frequently employed in order to solve problems derived from the management of shared distributed knowledge and the efficient integration of information across different applications. However, the process of ontology building is still a lengthy and error-prone task. Therefore, a number of research studies to (semi-)automatically build ontologies from existing documents have been developed. In this paper, we present our approach to extract relevant ontology concepts and their relationships from a knowledge base of heterogeneous text documents. We also show the architecture of the implemented system and discuss the experiments in a real-world context.

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.

How to integrate technology‐enhanced learning with business process management

Purpose – The purpose of this paper is to propose an innovative approach for providing an answer to the emerging trends on how to integrate e-learning efficiently in the business value chain in medium and large enterprises. Design/methodology/approach – The proposed approach defines methodologies and technologies for integrating technology-enhanced learning with knowledge and human resources management based on a synergistic use of knowledge models, methods, technologies and approaches covering different steps of the knowledge life-cycle. Findings – The proposed approach makes explicit and supports, from the methodological, technological and organizational points of view, mutual dependencies between the enterprise’s organizational learning and the business processes, considering also their integration in order to allow the optimization of employees’ learning plans with respect to business processes and taking into account competencies, skills, performances and knowledge available inside the organization. Practical implications – This mutual dependency, bridging individual and organizational learning, enables an improvement loop to become a key aspect for successful business process improvement (BPI) and business process reengineering (BPR), enabling closure of, at the same time, the learning and knowledge loops at individual, group and organization levels. Originality/value – The proposed improvements are relevant with respect to the state of the art and respond to a real need felt by enterprises and further commercial solutions and research projects on the theme.

A computational study of local search algorithms for Italian high-school timetabling

Abstract In this paper we report on a computational experience with a local search algorithm for High-school Timetabling Problems. The timetable has to satisfy “hard” requirements, that are mandatory, and should minimize the violation of “soft” constraints. In our approach, we combine Simulated Annealing with a Very Large-Scale Neighborhood search where the neighborhood is explored by solving an Integer Programming problem. We report on a computational experience validating the usefulness of the proposed approach.

The Stationary Characteristics of the G / MSP /1/ r Queueing System

A single-server queueing system with recurrent input flow and Markov service process is considered. Both the cases of finite and infinite buffers are investigated. The analysis of this system is based on the method of embedded Markov chain. The main stationary characteristics of system performance are derived.

Optimizing learning path selection through memetic algorithms

e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way.

Elicitation of latent learning needs through learning goals recommendation

The aim of a recommender system is to estimate the relevance of a set of objects belonging to a given domain, starting from the information available about users and objects. Adaptive e-learning systems are able to automatically generate personalized learning experiences starting from a learner profile and a set of target learning goals. Starting form research results of these fields we defined a methodology and developed a software prototype able to recommend learning goals and to generate learning experiences for learners using an adaptive e-learning system. The prototype has been integrated within IWT: an existing commercial solution for personalized e-learning and experimented in a graduate computer science course.

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.

Hybrid methodologies to foster ontology-based knowledge management platform

Nowadays, a multitude of users benefits from social interactions, blogging, wiki in order to share their own contents with each other (i.e., user-generated content). In fact, both Web 2.0 and Enterprise 2.0 applications have changed the knowledge sharing paradigm, and have introduced enabling features to foster information flow among users. Nevertheless, the availability of large amount of information targeted to human employment highlights reusing, reasoning and exploitation of available knowledge. Emerging Semantic Web technologies enable to codify information in a machine understandable way. Therefore, the latest web development trend is devoted to combine Web 2.0 features with semantic technologies (e.g. semantic tagging, semantic wiki). This scenario raises new requirements in terms of knowledge base extraction, update and maintenance. To this end, this work defines an ontology-based knowledge management platform that integrates methodologies aimed at supporting the life cycle of large and heterogeneous enterprises knowledge bases. In particular, the defined architecture relies on hybrid methodologies which apply computational intelligence techniques and Semantic Web technologies to support Knowledge Extraction, Ontology Matching and Ontology Merging.

A LP-based heuristic for a time-constrained routing problem

Abstract In this paper we present a LP-based heuristic for the solution of a Time Constrained Routing problem arising from innovative services accessible via World Wide Web. The problem consists of scheduling the visit of a tourist to a given geographical area in order to maximize his satisfaction degree whilst respecting time windows restrictions. We refer to this problem as the Intelligent Tourist Problem (ITP). ITP is formulated as a Set Packing problem with side constraints. Due to the huge number of variables in the formulation, the LP-relaxation is solved by a “column-and-row generation” approach. Then we run a MIP solver over the active columns to get a feasible solution. Computational experience on real-world instances is reported showing the effectiveness of the proposed approach.