Soft computing agents for e-health applied to the research and control of unknown diseases

This paper presents an Ontology-based Holonic Diagnostic System (OHDS) that combines the advantages of the holonic paradigm with multi-agent system technology and ontology design, for the organization of unstructured biomedical research into structured disease information. We use ontologies as 'brain' for the holonic diagnostic system to enhance its ability to structure information in a meaningful way and share information fast. To integrate dispersed heterogeneous knowledge available on the web we use a fuzzy mechanism ruled by intelligent agents, which automatically structures the information in the adequate ontology template. Our vision of how this system implementation should be backed by a solid security shield that ensures the privacy and safety of medical information concludes the paper

On the Role of Context and Subjectivity on Scientific Information Systems

The explicit representation of context and subjectivity enables an information system to support multiple interpretations of the data it records. This is a crucial aspect of learning and innovation within scientific information systems. We present an ontology-based framework for context and subjectivity that integrates two lines of research: data provenance and ontological foundations of the Semantic Web. Data provenance provides a set of constructs for representing data history. We extend the definition of these constructs in order to describe multiple viewpoints or interpretations held within a domain. The W7 model, the Toulmin model, and the Proof Markup Language (PML) provide the Interlingua for creating multiple viewpoints of data in a machine-readable and sharable form. Example use cases in space sciences are used to demonstrate the feasibility and value of our approach

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

An ontological framework for the formal representation and management of human stress knowledge

There is a great deal of information on the topic of human stress which is embedded within numerous papers across various databases. However, this information is stored, retrieved, and used often discretely and dispersedly. As a result, discovery and identification of the links and interrelatedness between different aspects of knowledge on stress is difficult. This restricts the effective search and retrieval of desired information. There is a need to organize this knowledge under a unifying framework, linking and analysing it in mutual combinations so that we can obtain an inclusive view of the related phenomena and new knowledge can emerge. Furthermore, there is a need to establish evidence-based and evolving relationships between the ontology concepts.Previous efforts to classify and organize stress-related phenomena have not been sufficiently inclusive and none of them has considered the use of ontology as an effective facilitating tool for the abovementioned issues.There have also been some research works on the evolution and refinement of ontology concepts and relationships. However, these fail to provide any proposals for an automatic and systematic methodology with the capacity to establish evidence-based/evolving ontology relationships.In response to these needs, we have developed the Human Stress Ontology (HSO), a formal framework which specifies, organizes, and represents the domain knowledge of human stress. This machine-readable knowledge model is likely to help researchers and clinicians find theoretical relationships between different concepts, resulting in a better understanding of the human stress domain and its related areas. The HSO is formalized using OWL language and Protégé tool.With respect to the evolution and evidentiality of ontology relationships in the HSO and other scientific ontologies, we have proposed the Evidence-Based Evolving Ontology (EBEO), a methodology for the refinement and evolution of ontology relationships based on the evidence gleaned from scientific literature. The EBEO is based on the implementation of a Fuzzy Inference System (FIS).Our evaluation results showed that almost all stress-related concepts of the sample articles can be placed under one or more category of the HSO. Nevertheless, there were a number of limitations in this work which need to be addressed in future undertakings.The developed ontology has the potential to be used for different data integration and interoperation purposes in the domain of human stress. It can also be regarded as a foundation for the future development of semantic search engines in the stress domain

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Collaborative action research for the governance of climate adaptation - foundations, conditions and pitfalls

This position paper serves as an introductory guide to designing and facilitating an action research process with stakeholders in the context of climate adaptation. Specifically, this is aimed at action researchers who are targeting at involving stakeholders and their expert knowledge in generating knowledge about their own condition and how it can be changed. The core philosophy of our research approach can be described as developing a powerful combination between practice-driven collaborative action research and theoretically-informed scientific research. Collaborative action research means that we take guidance from the hotspots as the primary source of questions, dilemmas and empirical data regarding the governance of adaptation, but also collaborate with them in testing insights and strategies, and evaluating their usefulness. The purpose is to develop effective, legitimate and resilient governance arrangements for climate adaptation. Scientific quality will be achieved by placing this co-production of knowledge in a well-founded and innovative theoretical framework, and through the involvement of the international consortium partners. This position paper provides a methodological starting point of the research program ‘Governance of Climate Adaptation’ and aims: · To clarify the theoretical foundation of collaborative action research and the underlying ontological and epistemological principles · To give an historical overview of the development of action research and its different forms · To enhance the theoretical foundation of collaborative action research in the specific context of governance of climate adaptation. · To translate the philosophy of collaborative action research into practical methods; · To give an overview of the main conditions and pitfalls for action research in complex governance settings Finally, this position paper provides three key instruminstruments developed to support Action Research in the hotspots: 1) Toolbox for AR in hotspots (chapter 6); 2) Set-up of a research design and action plan for AR in hotspots (chapter 7); 3) Quality checklist or guidance for AR in hotspots (chapter 8)