Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

Universal access in the information society

Internet-of-Things (IoT) is the convergence of Internet with RFID, Sensor and smart objects. IoT can be defined as “things belonging to the Internet” to supply and access all of real-world information. Billions of devices are expected to be associated into the system and that shall require huge distribution of networks as well as the process of transforming raw data into meaningful inferences. IoT is the biggest promise of the technology today, but still lacking a novel mechanism, which can be perceived through the lenses of Internet, things and semantic vision. This paper presents a novel architecture model for IoT with the help of Semantic Fusion Model (SFM). This architecture introduces the use of Smart Semantic framework to encapsulate the processed information from sensor networks. The smart embedded system is having semantic logic and semantic value based Information to make the system an intelligent system. This paper presents a discussion on Internet oriented applications, services, visual aspect and challenges for Internet of things using RFID, 6lowpan and sensor networks.

A survey of Internet-of-Things: Future vision, architecture, challenges and services

1 Peer Learning with Computers.- Collaborative problem solving with HyperCard: The influence of peer interaction on planning and information handling strategies.- Small group collaborative discovery learning from hypertext.- Peer interaction and writing: The process of revision.- Computer support for the collaborative learning of physics concepts.- The construction of shared knowledge in collaborative problem solving.- 2 Computer Support for Distance Learning.- Learning network design: Coordinating group interactions in formal learning environments over time and distance.- Computer-supported collaborative learning in a multi-media distance education environment.- Distance learning and computer-mediated communication: Interactive, quasiinteractive or monologue?.- 3 The Social and Organisational Context.- Educational practice within two local computer networks.- Technology's role in restructuring for collaborative learning.- 4 Models of Collaboration.- The negotiation of dialogue focus: An investigation of dialogue processes in joint planning in a computer based task.- Computational modelling of constructive interaction: Relaxing the mutuality hypothesis.- Designing human-computer collaborative learning.- 5 Design Issues.- Issues in computer supported collaborative learning.- Designing computer support for collaborative learning.

Computer supported collaborative learning

Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.

Ontology learning from text: A look back and into the future

The fast emergent and continuously evolving areas of the Semantic Web and Knowledge Management make the incorporation of ontology engineering tasks in knowledge-empowered organizations and in the World Wide Web more than necessary. In such environments, the development and evolution of ontologies must be seen as a dynamic process that has to be supported through the entire ontology life cycle, resulting to living ontologies. The aim of this paper is to present the Human-Centered Ontology Engineering Methodology (HCOME) for the development and evaluation of living ontologies in the context of communities of knowledge workers. The methodology aims to empower knowledge workers to continuously manage their formal conceptualizations in their day-to-day activities and shape their information space by being actively involved in the ontology life cycle. The paper also demonstrates the Human Centered ONtology Engineering Environment, HCONE, which can effectively support this methodology.

Human-centered ontology engineering: The HCOME methodology

The aim of this paper is to present an innovative approach for generating multiple choice questions in automatic way. Although other approaches have been already reported in the literature, the approach presented in this paper is based on domain specific ontologies and it is independent of lexicons such as WordNet or other linguistic resources. The paper also reports on a first prototype implementation of such an approach which creates multiple choice question items using the Semantic Web standard technology OWL (Ontology Web Language). The proposed approach is independent of the domain since questions are generated according to specific ontology-based strategies. In current implementation, simple natural language generation techniques are used in order to provide the items in the questionnaires. KEYWORDS Ontologies, multiple choice questions, assessment 1. INTRODUCTION Multiple choice questions (MCQ) are a very popular means of assessment and self-assessment in both traditional and electronic learning settings. They are appealing to the examinees, they can be automatically graded and they provide the capability of frequent testing, almost immediate feedback on the performance. An MCQ questionnaire comprises a number of questions named

Automatic generation of multiple choice questions from domain ontologies

Towards automatic merging of domain ontologies: The HCONE-merge approach

Existing efforts on ontology mapping, alignment and merging vary from methodological and theoretical frameworks, to methods and tools that support the semi-automatic coordination of ontologies. However, only latest research efforts ”touch” on the mapping/merging of ontologies using the whole breadth of available knowledge. This paper aims to thoroughly describe the HCONE approach on ontology merging. The approach described is based on (a) capturing the intended informal interpretations of concepts by mapping them to WordNet senses using lexical semantic indexing, and (b) exploiting the formal semantics of concepts’ definitions by means of description logics’ reasoning services.

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The HCONE approach to ontology merging

The aim of this paper is to present authors' proposal regarding semantic interoperability for interconnected and semantically coordinated smart entities in a Web of Things More specific, the paper presents a use case scenario and requirements related to the semantic registration, coordination and retrieval of smart entities Motivated by these, the paper accentuates the need for, and emphasizes, a framework of Semantic Smart Gateways (SSGF) in the Semantic Web of Things (SWoT), proposing an ontology learning and an ontology alignment method respectively

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Konstantinos Kotis

Papers

Human-centered ontology engineering: The HCOME methodology

Automatic generation of multiple choice questions from domain ontologies

Towards automatic merging of domain ontologies: The HCONE-merge approach

The HCONE approach to ontology merging

Semantic Interoperability on the Web of Things: The Semantic Smart Gateway Framework