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Special issue on Systems Development by Means of Semantic Technologies
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Science of Computer Programming ••• (••••) •••–•••
Contents lists available at ScienceDirect
Science of Computer Programming www.elsevier.com/locate/scico
Preface
Special issue on Systems Development by Means of Semantic Technologies Semantic, from the Greek “semantikos”, involves giving significance or meaning to words or symbols, thus enabling distinctions to be made between the meanings of different words or symbols. Semantic technologies therefore provide a consistent and reliable basis that can be used to confront the challenges related to the organization, manipulation and visualization of data and knowledge, in addition to playing a crucial role as the technological basis in the development of a large number of computational intelligence systems. These technologies draw on both standard and new techniques from various disciplines within Computer Science, including Knowledge Engineering, Natural Language Processing, Artificial Intelligence, Databases, Software Agents, etc. The methods and tools developed and integrated for this purpose are generic and have a very large application potential in many fields such as Information Retrieval, Semantic Searches, Information Integration, Information Interoperability, Bioinformatics, eHealth, eLearning, Software Engineering, eCommerce, eGovernment, Social Networks, etc. In this scenario, the aim of this special issue in the Science of Computer Programming journal was to collect innovative and high-quality research contributions regarding the role played by Semantic Technologies in computational systems, paying special attention to programming techniques and tools. Contributions in the form of theoretical and experimental research and case studies were welcomed. The editors have been able to bring together submissions from researchers with a common interest in the use and application of programming techniques for the development of research tools and approaches based on semantic technologies. The original Call for Papers led to the submission of twenty two research papers of which seven were eventually accepted for publication. In the first paper, entitled An Extensible Argument-based Ontology Matching Negotiation Approach, Maio et al. based their research on the well-known problem of the various communication schemes that exist between arbitrary systems. These schemes and semantics are usually provided by ontologies using specific semantics provided by the ontology language and based on the criteria of the system designers. However, computational systems very often adopt different ontologies in order to describe their domain of discourse, resulting in conflicts. To address these conflicts, computational systems should engage in some kind of negotiation process that makes it possible to reach a common agreement. In this paper, the authors propose an argumentation-based approach in which the computational entities describe their own arguments according to a commonly agreed argumentation meta-model. In the second contribution, entitled A Domain-Independent Process for Automatic Ontology Population from Text, Faria et al. address the problem of ontology population. Ontology population makes reference to the process of instantiating the basic elements of an ontology, such as properties and non-taxonomic relationships. In this context, manual population by domain experts and knowledge engineers is an expensive and time consuming task. Given that fast ontology population is crucial for the success of knowledge-based applications, automatic or semi-automatic approaches that are capable of populating ontologies are needed. In this work, the authors propose a generic process in which the Automatic Ontology Problem is approached by specifying the phases and the techniques used to perform the activities in each phase. The main contribution is a domain-independent process for the automatic population of ontologies from text by means of natural language processing and information extraction techniques. In the third paper, entitled Cultural Scene Detection Using Reverse Louvain Optimization, Hamdaqa et al. present a new approach with which to discover cultural scenes in social network data. The term cultural scenes refers to the aggregation of people with overlapping interests, whose loosely interacting activities from virtuous cycles amplify cultural output (e.g., New York art scene, Silicon Valley startup scene, Seattle indie music scene). They are defined by time, place, topics, people and values. The contribution of this paper is three-fold: first, authors propose an ontology with which to represent cultural scenes, second, they map a dataset onto the ontology, and third, they compare two methods that can be used to detect scenes in the dataset. In the fourth contribution, entitled A Conceptual Model and Technological Support for Organizational Knowledge Management, Ale et al. have based their research on the assumption that Knowledge Management (KM) models proposed in previous http://dx.doi.org/10.1016/j.scico.2014.04.010 0167-6423/© 2014 Elsevier B.V. All rights reserved.
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Preface
literature do not take into account all of the aspects required for an effective knowledge management. In order to address this issue, the authors present a set of requirements that any KM model or initiative should take into account in order to cover all the aspects implied in knowing processes. As a second contribution, the authors present a new distributed KM Conceptual Model whose building blocks are the knowledge activities involved in knowledge processes. The paper also briefly describes an architecture with which to provide technological support for knowledge representation and the retrieval activities of the proposed KM Conceptual Model. Finally, based on the available literature, a comparative analysis of different KM models is presented. In the fifth paper, entitled An Automated Tool for Semantic Accessing to Formal Software Models, Wang et al. propose a methodology with tool support that can be used to automatically derive ontological metadata from formal software models and semantically describe them. The authors have based this research on the assumption that it is difficult to understand, incorporate and use different formal models consistently during the software development process, particularly in the case of large and complex software systems. The main argumentation of this problem is based on the complex mathematical nature of the formal methods used and the lack of tool support. Hence, with the success of the Semantic web as the next generation of Web technology, the authors claim that these technologies make it possible for both software engineers and machines to understand the content of formal models and support more effective software design in terms of understanding, sharing and reusing in a distributed manner through the creation of proper semantic metadata for software models and their related software artifacts. In the sixth contribution, entitled Exploiting Semantic Technologies in Smart Environments and Grids: Emerging Roles and Case Studies, Bonino et al. provide an overview of the roles played by semantic technologies in the domain of smart grids and smart environments, placing special emphasis on the changes brought about by such technologies in the architectures, programming techniques and tools adopted. Finally, Labra-Gayo et al. in the last paper, entitled Inductive Representations of RDF Graphs, propose a purely functional representation of RDF graphs using a special form of inductive graphs called inductive triple graphs. The authors employ logical variables to represent blank nodes. The papers presented in this special issue lead to a better understanding of the current interest, applications, and use of semantic technologies, with a particular emphasis on computer programming techniques and models. Last but not least, the guest editors would like to thank Jan Bergstra and Bas van Vlijmen for their endless support during the review process and giving us the opportunity to edit this special issue. The special issue editors are also very grateful to the reviewers who kindly agreed to referee the manuscripts in a timely manner, and provided the authors with valuable feedback. Finally, we commend the authors for their valuable contributions and insights. Acknowledgement The preparation of this special collection has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER/ERDF) through the SeCloud project (TIN2010-18650).
Rafael Valencia-García University of Murcia, Spain E-mail address: [email protected] Alejandro Rodríguez-González Centre for Plant Biotechnology and Genomics, Polytechnic University of Madrid, Spain E-mail address: [email protected] Ricardo Colomo-Palacios Faculty of Computer Sciences, Østfold University College, Norway E-mail address: [email protected] 2 April 2014 Available online xxxx
[m3G; v 1.133; Prn:8/05/2014; 9:33] P.1 (1-2)
Science of Computer Programming ••• (••••) •••–•••
Contents lists available at ScienceDirect
Science of Computer Programming www.elsevier.com/locate/scico
Preface
Special issue on Systems Development by Means of Semantic Technologies Semantic, from the Greek “semantikos”, involves giving significance or meaning to words or symbols, thus enabling distinctions to be made between the meanings of different words or symbols. Semantic technologies therefore provide a consistent and reliable basis that can be used to confront the challenges related to the organization, manipulation and visualization of data and knowledge, in addition to playing a crucial role as the technological basis in the development of a large number of computational intelligence systems. These technologies draw on both standard and new techniques from various disciplines within Computer Science, including Knowledge Engineering, Natural Language Processing, Artificial Intelligence, Databases, Software Agents, etc. The methods and tools developed and integrated for this purpose are generic and have a very large application potential in many fields such as Information Retrieval, Semantic Searches, Information Integration, Information Interoperability, Bioinformatics, eHealth, eLearning, Software Engineering, eCommerce, eGovernment, Social Networks, etc. In this scenario, the aim of this special issue in the Science of Computer Programming journal was to collect innovative and high-quality research contributions regarding the role played by Semantic Technologies in computational systems, paying special attention to programming techniques and tools. Contributions in the form of theoretical and experimental research and case studies were welcomed. The editors have been able to bring together submissions from researchers with a common interest in the use and application of programming techniques for the development of research tools and approaches based on semantic technologies. The original Call for Papers led to the submission of twenty two research papers of which seven were eventually accepted for publication. In the first paper, entitled An Extensible Argument-based Ontology Matching Negotiation Approach, Maio et al. based their research on the well-known problem of the various communication schemes that exist between arbitrary systems. These schemes and semantics are usually provided by ontologies using specific semantics provided by the ontology language and based on the criteria of the system designers. However, computational systems very often adopt different ontologies in order to describe their domain of discourse, resulting in conflicts. To address these conflicts, computational systems should engage in some kind of negotiation process that makes it possible to reach a common agreement. In this paper, the authors propose an argumentation-based approach in which the computational entities describe their own arguments according to a commonly agreed argumentation meta-model. In the second contribution, entitled A Domain-Independent Process for Automatic Ontology Population from Text, Faria et al. address the problem of ontology population. Ontology population makes reference to the process of instantiating the basic elements of an ontology, such as properties and non-taxonomic relationships. In this context, manual population by domain experts and knowledge engineers is an expensive and time consuming task. Given that fast ontology population is crucial for the success of knowledge-based applications, automatic or semi-automatic approaches that are capable of populating ontologies are needed. In this work, the authors propose a generic process in which the Automatic Ontology Problem is approached by specifying the phases and the techniques used to perform the activities in each phase. The main contribution is a domain-independent process for the automatic population of ontologies from text by means of natural language processing and information extraction techniques. In the third paper, entitled Cultural Scene Detection Using Reverse Louvain Optimization, Hamdaqa et al. present a new approach with which to discover cultural scenes in social network data. The term cultural scenes refers to the aggregation of people with overlapping interests, whose loosely interacting activities from virtuous cycles amplify cultural output (e.g., New York art scene, Silicon Valley startup scene, Seattle indie music scene). They are defined by time, place, topics, people and values. The contribution of this paper is three-fold: first, authors propose an ontology with which to represent cultural scenes, second, they map a dataset onto the ontology, and third, they compare two methods that can be used to detect scenes in the dataset. In the fourth contribution, entitled A Conceptual Model and Technological Support for Organizational Knowledge Management, Ale et al. have based their research on the assumption that Knowledge Management (KM) models proposed in previous http://dx.doi.org/10.1016/j.scico.2014.04.010 0167-6423/© 2014 Elsevier B.V. All rights reserved.
JID:SCICO AID:1757 /EDI
2
[m3G; v 1.133; Prn:8/05/2014; 9:33] P.2 (1-2)
Preface
literature do not take into account all of the aspects required for an effective knowledge management. In order to address this issue, the authors present a set of requirements that any KM model or initiative should take into account in order to cover all the aspects implied in knowing processes. As a second contribution, the authors present a new distributed KM Conceptual Model whose building blocks are the knowledge activities involved in knowledge processes. The paper also briefly describes an architecture with which to provide technological support for knowledge representation and the retrieval activities of the proposed KM Conceptual Model. Finally, based on the available literature, a comparative analysis of different KM models is presented. In the fifth paper, entitled An Automated Tool for Semantic Accessing to Formal Software Models, Wang et al. propose a methodology with tool support that can be used to automatically derive ontological metadata from formal software models and semantically describe them. The authors have based this research on the assumption that it is difficult to understand, incorporate and use different formal models consistently during the software development process, particularly in the case of large and complex software systems. The main argumentation of this problem is based on the complex mathematical nature of the formal methods used and the lack of tool support. Hence, with the success of the Semantic web as the next generation of Web technology, the authors claim that these technologies make it possible for both software engineers and machines to understand the content of formal models and support more effective software design in terms of understanding, sharing and reusing in a distributed manner through the creation of proper semantic metadata for software models and their related software artifacts. In the sixth contribution, entitled Exploiting Semantic Technologies in Smart Environments and Grids: Emerging Roles and Case Studies, Bonino et al. provide an overview of the roles played by semantic technologies in the domain of smart grids and smart environments, placing special emphasis on the changes brought about by such technologies in the architectures, programming techniques and tools adopted. Finally, Labra-Gayo et al. in the last paper, entitled Inductive Representations of RDF Graphs, propose a purely functional representation of RDF graphs using a special form of inductive graphs called inductive triple graphs. The authors employ logical variables to represent blank nodes. The papers presented in this special issue lead to a better understanding of the current interest, applications, and use of semantic technologies, with a particular emphasis on computer programming techniques and models. Last but not least, the guest editors would like to thank Jan Bergstra and Bas van Vlijmen for their endless support during the review process and giving us the opportunity to edit this special issue. The special issue editors are also very grateful to the reviewers who kindly agreed to referee the manuscripts in a timely manner, and provided the authors with valuable feedback. Finally, we commend the authors for their valuable contributions and insights. Acknowledgement The preparation of this special collection has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER/ERDF) through the SeCloud project (TIN2010-18650).
Rafael Valencia-García University of Murcia, Spain E-mail address: [email protected] Alejandro Rodríguez-González Centre for Plant Biotechnology and Genomics, Polytechnic University of Madrid, Spain E-mail address: [email protected] Ricardo Colomo-Palacios Faculty of Computer Sciences, Østfold University College, Norway E-mail address: [email protected] 2 April 2014 Available online xxxx