Maja Hadzic

Ontology-Based Multi-Agent Systems

During the last two decades, the idea of Semantic Web has received a great deal of attention. An extensive body of knowledge has emerged to describe technologies that seek to help us create and use aspects of the Semantic Web. Ontology and agent-based technologies are understood to be the two important technologies here. A large number of articles and a number of books exist to describe the use individually of the two technologies and the design of systems that use each of these technologies individually, but little focus has been given on how one can - sign systems that carryout integrated use of the two different technologies. In this book we describe ontology and agent-based systems individually, and highlight advantages of integration of the two different and complementary te- nologies. We also present a methodology that will guide us in the design of the - tegrated ontology-based multi-agent systems and illustrate this methodology on two use cases from the health and software engineering domain. This book is organized as follows: Chapter I, Current issues and the need for ontologies and agents, describes existing problems associated with uncontrollable information overload and explains how ontologies and agent-based systems can help address these - sues. Chapter II, Introduction to multi-agent systems, defines agents and their main characteristics and features including mobility, communications and collaboration between different agents. It also presents different types of agents on the basis of classifications done by different authors.

Spirituality and Mental Health: Current Research and Future Directions

Many researchers have demonstrated that spirituality plays a significant role in the lives of people, their thoughts, and behaviors. In this article, the effect of spirituality on mental health has been reviewed. In order to make further advances in the field it is becoming increasingly important to (a) acknowledge the differences between the different religions; (b) develop context-specific definitions of spirituality, namely define spirituality in the context of a particular faith; and (c) construct the studies that will evaluate spirituality according to the context-specific measures. This approach will enable us to carry out comprehensive studies of analytical and comparative nature, and generate groundbreaking results in this research field.

Application of Digital Ecosystem Design Methodology Within the Health Domain

We define a digital ecosystem (DES) as the dynamic and synergetic complex of digital communities consisting of interconnected, interrelated, and interdependent digital species situated in a digital environment that interact as a functional unit and are linked together through actions, information, and transaction flows. The design of DESs requires the integration of a number of different and complementary technologies, including agent-based and self-organizing systems, ontologies, swarm intelligence, ambient intelligence, data mining, genetic algorithms, etc. The integration of multiple technologies and the resulting synergetic effects contribute to the creation of highly complex, dynamic, and powerful systems. The application of DESs within different domains has the power to transform these domains by giving them a more intelligent and a more dynamic nature. In this paper, we illustrate how a DES design methodology can be used to systematically create a Digital Health Ecosystem (DHES). We address the key steps associated with the DES design and focus specifically on the use of the electronic health records within the DHES. The design methodology framework illustrated in this paper serves as a navigating tool during the design of DHESs.

Methodology framework for the design of digital ecosystems

Digital ecosystems have recently been introduced into the computer and information societies. Digital ecosystem (DES) is the dynamic and synergetic complex of digital communities consisting of interconnected, interrelated and interdependent digital species (DS) situated in a digital environment (DE) that interact as a functional unit and are linked together through actions, information and transaction flows. DES transpose mechanisms from living organisms like autonomy, viability and self-organisation to arrive at novel knowledge and architectures. The proposed DES embraces a number of different technologies such as ontologies, agent-based and self-organizing systems etc. The synergetic effects of these methodologies results in a more efficient, effective, reliable and secure system. DES is still in its early implementation phase. No clear methodology for digital ecosystem design exists yet. In this paper, we propose a methodological framework that consists of five phases and addresses many different aspects of the DES design. In this methodology, we focus on the key factors associated with the DES design such as roles of different digital components within the DES, organisation and collaboration of the digital components, their individual design along with intelligence and security within the DES. More details are introduced at each step and every sequential step takes the DES to a higher level of elaboration. This methodological framework allows better control over the design process and serves as a navigating tool during DES design.

Tree Mining in Mental Health Domain

The number of mentally ill people is increasing globally each year. Despite major medical advances, the identification of genetic and environmental factors responsible for mental illnesses still remains unsolved and is therefore a very active research focus today. Semi-structured data structure is predominantly used to enable the meaningful representations of the available mental health knowledge. Data mining techniques can be used to efficiently analyze these semi-structured mental health data. Tree mining algorithms can efficiently extract frequent substructures from semi-structured knowledge representation such as XML. In this paper we demonstrate effective application of the tree mining algorithms on records of mentally ill patients. The extracted data patterns can provide useful information to help in prevention of mental illness and assist in delivery of effective and efficient mental health services.

Use of Digital Ecosystem and Ontology Technology for Standardization of Medical Records

In this paper, we propose a medical records digital ecosystem (MRDES) that enables efficient use of medical records for the purpose of correct patient identification, diagnosis, appointments scheduling and the like, in everyday life as well as in emergency situations. Medical records digital environment (MRDE) is populated by interconnected medical records digital components (MRDC). MRDC are combined together to support creation of a digital infrastructure that will provide standardized use of medical records within various regions, countries and even continents. If medical records are to be recognized by different digital components at different locations, standardization of data needs to take place. Ontologies can be used for this purpose. Instantiation of the Generic Medical Record Ontology concepts result in Specific Medical Record Ontologies that act as personalized medical records. Ontology files are machine readable and are suitable to be used within MRDES. One of the key MRDC includes medical record databases that contain personal medical records. Through use of ontologies for standardization of medical records from these different databases, one big virtual database is created that contains medical records of all people. Other key MRDCs are machine-readable personal medical records as well as screening components that read those records. The advantage is that one comprehensive format for the medical records is set within this digital ecosystem and this record can be accessed, read and understood regardless of what country this person is in. This advantage is of especial importance in emergency cases. The significance of this research lies in the unification of the advances of the ontology technology and ecosystem paradigm for the purpose of establishing worldwide standardization of medical records.

Role of the Ontologies in the Context of Grid Computing and Application for the Human Disease Studies

We describe the increasing impact of ontologies in the context of Grid computing for obtaining, comparing and analyzing distributed heterogeneous scientific data. The inherently autonomous and heterogeneous nature of the information resources forces applications to share data and services often without prior knowledge of their structure and functionality, respectively. Ontologies are needed to provide a way to capture and present in the computer, knowledge shared by all people in a certain community. Computer based ontologies may be seen as shared formal conceptualizations of domain knowledge and therefore constitute an essential resource for enabling interoperation in an open environment such as the Web on the Grid. We illustrate how ontologies can be developed for the knowledge domain of biomedical and bio-engineering research. We chose the application domain of human disease research and control since it necessarily involves resources of phenotypic, genetic, environmental and treatment data.

Designing the human stress ontology: A formal framework to capture and represent knowledge about human stress

Abstract A large number of papers have been published on the topic of human stress. There is a need to organise this knowledge under a unifying framework, linking and analysing it in mutual combinations so that new knowledge can emerge. Ontology is an enriched conceptual model for representing domain knowledge. An ontology can be designed to provide a framework for knowledge about human stress. This agreed knowledge model will facilitate knowledge sharing and communications. Additionally, ontologies are machine-readable and can enable automated programs such as data mining to intelligently access and analyse information. The purpose of this paper is to discuss and explain some of the common stress-related terms, their definitions, classifications and possible interrelations between them. A top-layer of the human stress ontology (HSO) model is presented, which will continue to evolve as more research contributions and knowledge become available. It is believed that this framework can be used to build power...

Towards the Mental Health Ontology

Lots of research have been done within the mental health domain, but exact causes of mental illness are still unknown. Concerningly, the number of people being affected by mental conditions is rapidly increasing and it has been predicted that depression would be the worlds leading cause of disability by 2020. Most mental health information is found in electronic form. Application of the cutting-edge information technologies within the mental health domain has the potential to greatly increase the value of the available information. Specifically, ontologies form the basis for collaboration between research teams, for creation of semantic Web services and intelligent multi-agent systems, for intelligent information retrieval, and for automatic data analysis such as data mining. In this paper, we present mental health ontology which can be used to underpin a variety of automatic tasks and positively transform the way information is being managed and used within the mental health domain.

Disease ontology based grid middleware for human disease research study

This paper describes disease ontology based grid middleware which acts like an intelligent search engine, receiving requests from customers and finding services or service providers for them. We note that currently no such work has been done or successfully implemented. We propose the use of grid middleware for information exchange as it is able to access unstructured petabyte information on the Web and pull the right information for the user. Before it can do this, it obtained the knowledge from ontologies such as medical issues, health matters, disease factors, DNA etc and knows who is doing a particular research, what work has been done and which research group have the most up-to-date results, which database in the Web is needed, what is in it, what is the value of the information in that database, where it fits into the specific disease knowledge and how to access it, whose work relates to each others, overlapping with or complements each others etc. It helps search, translate, categorize, index (through ontology and agents), download and upload the exact disease information that the end-user requires.