Semantic Innovation Management

Innovation within industrial environment can be viewed as a cyclic loop consisting of four distinct phases, i.e., recognition, initiation, implementation, and stabilization. Different information technology enabled innovation management tools supporting the lifecycle of innovation are classified as five layers, i.e., individual innovation, project innovation, collaborative innovation, distributed innovation, and semantic innovation. According the fact that the current state is evolving from distributed innovation to semantic innovation, this paper focus on the realization of Semantic Web technologies enabled semantic innovation. To explicitly and formally specify all the different perspectives of innovation related information, a shared ontology is proposed as the common language of innovation management, which describes the critical and minimal information about the innovation process in a holistic way. Then, a technical framework which employs the machine readable innovation ontology to actually improve innovation management inside an organization and among loosely coupled organizations is presented. Finally, some features of the semantic innovation are discussed

The role of community and social metrics in ontology evaluation: An interview study of ontology reuse

Finding a "good" or the "right" ontology for reuse is an ongoing challenge in the field of ontology engineering, where the main aim is to share and reuse existing semantics. This paper reports on a qualitative study with interviews of ontologists and knowledge engineers in different domains, ranging from biomedical field to manufacturing industry, and investigates the challenges they face while searching, evaluating, and selecting an ontology for reuse. Analysis of the interviews reveals diverse sets of quality metrics that are used when evaluating the quality of an ontology. While some of the metrics have already been mentioned in the literature, the findings from our study identify new sets of quality metrics such as community and social related metrics. We believe that this work represents a noteworthy contribution to the field of ontology engineering, with the hope that the research community can further draw on these initial findings in developing relevant quality metrics and ontology search and selection

Ontology and the Semantic Web

This paper discusses the development of a new information representation system embodied in ontology and the Semantic Web. The new system differs from other representation systems in that it is based on a more sophisticated semantic representation of information, aims to go well beyond the document level, and designed to be understood and processed by machine. A common theme underlying these three features, i.e., turning documents into meaningful interchangeable data, reflects a rising use expectation nurtured by modern technology and, at the same time, presents a unique challenge for its enabling technologies

Institutionalising Ontology-Based Semantic Integration

We address what is still a scarcity of general mathematical foundations for ontology-based semantic integration underlying current knowledge engineering methodologies in decentralised and distributed environments. After recalling the first-order ontology-based approach to semantic integration and a formalisation of ontological commitment, we propose a general theory that uses a syntax-and interpretation-independent formulation of language, ontology, and ontological commitment in terms of institutions. We claim that our formalisation generalises the intuitive notion of ontology-based semantic integration while retaining its basic insight, and we apply it for eliciting and hence comparing various increasingly complex notions of semantic integration and ontological commitment based on differing understandings of semantics

GAOM: Genetic Algorithm Based Ontology Matching

In this paper a genetic algorithm-based optimization procedure for ontology matching problem is presented as a feature-matching process. First, from a global view, we model the problem of ontology matching as an optimization problem of a mapping between two compared ontologies, and every ontology has its associated feature sets. Second, as a powerful heuristic search strategy, genetic algorithm is employed for the ontology matching problem. Given a certain mapping as optimizing object for GA, fitness function is defined as a global similarity measure function between two ontologies based on feature sets. Finally, a set of experiments are conducted to analysis and evaluate the performance of GA in solving ontology matching problem

The Semantic Web in Federated Information Systems: A Space Physics Case Study

This paper presents a new theoretical contribution that provides a middle-of-the-road approach to formal ontologies in federated information systems. NASA’s space physics domain, like many other domains, is relatively unfamiliar with the emerging Semantic Web. This work offers a new framework that garners the benefits of formal logic yet shields participants and users from the details of the technology. Moreover, the results of a case study involving the utilization of the Semantic Web within NASA’s space physics domain are presented. A real-world search and retrieval system, relying on relational database technology, is compared against a near identical system that incorporates a formal ontology. The efficiency, efficacy, and implementation details of the Semantic Web are compared against the established relational database technology

OBSERVAÇÕES PARA UMA POLÍTICA DE PESQUISA EM CIÊNCIA DA INFORMAÇÃO NO BRASIL

Discute as perspectivas da pesquisa em Ciência da Informação no Brasil, a partir da experiência pessoal, dos desafios que são colocados para a Ciência da Informação pelo surgimento da Internet e da Sociedade da Informação. Avalia os mecanismos institucionais de pesquisa e pós-graduação da área no Brasil e levanta questões e sugestões. Palavras-chave: Ciência da Informação, pesquisa, pós-graduação, Brasil

Discovery-driven ontology evolution

In this paper, we present a methodology for ontology evolution, by focusing on the specific case of multimedia ontology volution. In particular, we discuss the situation where the ontology needs to be enriched because it does not contain any concept that could be used to explain a new multimedia resource. The paper shows how ontology matching techniques can be used to enforce the discovery of new relevant concepts by probing external knowledge sources using both the information available in the multimedia resource and the knowledge contained in the current version of the ontology