A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Configuring value networks based on subjective business values

Monetary profitability is an objective value essential to the sustainability of a value network. The analysis of this requirement continues to receive substantial attention by the e value research community thus far. However, subjective values such as privacy, security and trust might also play a key role on the configuration of a value network, especially when it is necessary to differentiate equivalent monetary value propositions. This paper describes an ontological proposition for configuring value networks based on subjective values. The ontology is aimed to be used as complement of the e value framework, blending concepts of Multiple Agency Theory, Enterprise Ontology, Value Modeling and Speech Acts Theory. We demonstrate our approach on a case scenario based on the Directive 2009/72/EC, which defines common rules for the liberalization of the European market of energy

Health Improvement Path: Ontological Approach to Self-management Support in Personal Health Management Systems

Ontologies have been used for knowledge modeling and reasoning in healthcare domain (e.g., homecare, hospital clinical procedure, mHealth, etc.), but few in a context of self-management in healthcare with no sufficient reasoning rules to specify a systematic health management plan for an individual. In response to such needs, we aim to provide a generic ontology model for organizing the broad range of multidisciplinary knowledge required in personal health management by applying the ontology design patterns as well as for being extensible to more specific activity ontologies (e.g., physical exercises, diet, medication intake, etc.). The scope of a proposed ontology is to classify core concepts and relations in health self-management process and to build axioms for health improvement plans to meet an individual’s needs and health capability/maturity level. The proposed ontology is developed based on our previous work, health capability maturity model (HCMM) and can be integrated with existing health-related ontologies for further specification in health management processes

Towards ensuring Satisfiability of Merged Ontology

AbstractThe last decade has seen researchers developing efficient algorithms for the mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. Our methodology detects inconsistencies based on structural mismatches that occur due to conflicts among the set of Generalized Concept Inclusions, and Disjoint Relations due to the differences between disjoint partitions in the local heterogeneous ontologies. We present novel methodologies to detect and repair semantic inconsistencies from the list of initial mappings. This results in global merged ontology free from ‘circulatory error in class/property hierarchy’, „common class/instance between disjoint classes error’, ‘redundancy of subclass/subproperty relations’, ‘redundancy of disjoint relations’ and other types of „semantic inconsistency’ errors. In this way, our methodology saves time and cost of traversing local ontologies for the validation of mappings, improves performance by producing only consistent accurate mappings, and reduces the user dependability for ensuring the satisfiability and consistency of merged ontology. The experiments show that the newer approach with automatic inconsistency detection yields a significantly higher precision

Ontology selection for reuse: Will it ever get easier?

Ontologists and knowledge engineers tend to examine different aspects of ontologies when assessing their suitability for reuse. However, most of the evaluation metrics and frameworks introduced in the literature are based on a limited set of internal characteristics of ontologies and dismiss how the community uses and evaluates them. This paper used a survey questionnaire to explore, clarify and also confirm the importance of the set of quality related metrics previously found in the literature and an interview study. According to the 157 responses collected from ontologists and knowledge engineers, the process of ontology selection for reuse depends on different social and community related metrics and metadata. We believe that the findings of this research can contribute to facilitating the process of selecting an ontology for reuse