As medical research becomes ever finer-grained, experiments require healthcare data in quantities that single countries cannot provide.Cross-jurisdictional data collection remains, however, extremely challenging due to the diverging legal, professional, linguistic, normative, and technological contexts of the participating countries.Medical data heterogeneity, in particular, is still a largely unsolved problem on the international level, due to the complexity of data combined with strict precision and data protection constraints.We propose a scalable solution based on a novel knowledge architecture and the corresponding knowledge graph integration methodology.Medical knowledge that drives the scalable integration process is divided into multiple functional layers and is maintained in a distributed manner across participating countries.We successfully applied the approach in the context of a research experiment across Scotland and Italy, and are currently adapting it within other initiatives of Europe-wide health data interoperability. Using Multi-Layered and Distributed Knowledge

Understanding and predicting the pattern formation in groups of pluripotent stem cells has the potential to improve efficiency and efficacy of stem cell therapies. However, the underlying molecular mechanisms of pluripotent stem cell behaviors are highly complex and are currently still not fully understood. A key practical question is whether deep biological modelling of the cells is essential to predict their pattern formation, or whether there is sufficient predictive power in simply modelling their behaviors and interactions at a higher level. This study focuses on the social interactions and behaviors of pluripotent stem cells at a high-level to predict aggregate crowd behaviors within a level of uncertainty. Agent-based modelling was applied to study the pattern formation in pluripotent stem cells. Five models were established to test four biologically plausible rules of cell motility in terms of: a) velocity, b) directional persistence time, c) directional movements, and d) border effect. We found that it is possible that cells' directional movements based on local density play an important role of the pattern formation, and pattern formation in pluripotent stem cells is governed by a complex combination of rules in our agent-based model simulations, which account for much of the variability observed in experimental findings.

Today's crowdsourcing systems are predominantly used for processing independent tasks with simplistic coordination. As such, they offer limited support for handling complex, intellectually and organizationally challenging labour types, such as software development. In order to support crowd

This editorial introduction first outlines some of the research challenges raised by the emerging forms of internet-scale human problem solving. It then explains how the two articles in this special section can serve as illuminating complementary case studies, providing concrete examples embedded in general conceptual frameworks.

The objective of this paper is to try to fill the gap between: argumentation, electronic institutions and protocols by using a combination of automated synthesis and model checking methods. More precisely, this paper proposes a means of moving rapidly from argument specification to protocol implementation, using an extension of the Argument Interchange Format as the specification language and the Lightweight Coordination Calculus as an implementation language.

Little attention has been paid to the relationship between fitness evaluation in evolutionary algorithms and reputation mechanisms in multi-agent systems, but if these could be related it opens the way for implementation of distributed evolutionary systems via multi-agent architectures. In this paper we investigate the effectiveness with which reputation can replace direct fitness observation as the selection bias in an evolutionary multi-agent system. We do this by implementing a peer-to-peer, self-adaptive genetic algorithm, in which agents act as individual GAs that, in turn, evolve dynamically themselves in real-time. The evolution of the agents is implemented in two alternative ways: First, using the traditional approach of direct fitness observation (self-reported by each agent), and second, using a simple reputation model based on the collective past experiences of the agents. Our research shows that this simple model of distributed reputation can be successful as the evolutionary drive in such a system. Further, we discuss the effect of noise (in the form of "defective" agents) in both models. We show that, unlike the fitness-based model, the reputation-based model manages to identify the defective agents successfully, thus showing a level of resistance to noise.

Several solutions to describing service choreography have emerged, mainly focused on encoding capabilities of services especially for those deployed on the Web. These solutions are either derived from traditional Web service standards such as WSDL or inspired by the theory of process calculus. Little attention has however been paid to finding a lightweight solution which can enable peers to obtain, publish and share service choreography in an open environment or peer-to-peer network. This paper proposes a framework for choreographing semantically enhanced Web Services encoded in a extended lightweight coordinative language which is derived from process calculus and is dedicated to running in modern Web browsers. A proof-of-concept prototype has been implemented and demoed as a decentralised service choreography-management platform based on this framework. There is no need for users to install any third-party application, and service choreography execution is achieved via client-side Web browsers. Also, the preliminary experiments indicate the efficiency and scalability of our proof-of-concept implementation of this framework.

We address the problem of how to reason about properties of knowledge transformations as they occur in distributed and decentralized interactions between large and complex artifacts, such as databases, web services, and ontologies. Based on the conceptual distinction between specifications of interactions and properties of knowledge transformations that follow from these interactions, we explore a novel mixture of process calculus and property inference by connecting interaction models with knowledge transformation rules. We aim at being generic in our exploration, hence our emphasis on abstract knowledge transformations, although we exemplify it using a lightweight specification language for interaction modeling (for which an executable peer-to-peer environment already exists) and provide a formal semantics for knowledge transformation rules using the theory of institutions. Consequently, our exploration is also an example of the gain obtained by linking current state-of-the-art distributed knowledge engineering based on web services and peer-based architectures with formal methods drawn from a long tradition in algebraic specification.

This paper describes the development of a distributed multi-agent workflow enacting mechanism starting from a BPEL4WS specification. Our work demonstrates that a multi-agent interaction protocol (Lightweight Coordination Calculus) can be derived from

Software systems must be capable of coping with continuous requirements changes and at the same time wisely make use of emerging components and services to remain useful in their environment. In this paper, the adaptive agent model (AAM) approach is proposed. The AAM uses configurable interaction model to drive adaptive agent behaviour. The model captures user requirements and is maintained by experts at a high level of abstraction. The AAM interaction model has been discussed with regard to interaction specification and interaction coordination, in line with a coordination language for the OpenKnowledge project. A major benefit of using the approach is agents can dynamically choose disparate components and services already developed for computation via their interaction with each other at runtime, when a new interaction model has been configured for them towards an emerging business goal. A simple expert seeking scenario has been used to illustrate the approach.

The purpose of OWL-S is to support effective automation of various Web services related activities including service discovery, composition, execution and monitoring. For our work, we are interested in automated Web services composition for a business process model. OWL-S process ontology provides a standard language for describing the composition of Web services. Thus we can treat composed Web services as a process model. However, since OWL-S is an XML based syntax description which is used for automatic agent processing, it is difficult to read and understand for human reader, especially for those with little XML knowledge but from a process modelling background, for example, businessmen. Therefore it can not be easily and directly used for process modelling tasks. The Fundamental Business Process Modelling Language (FBPML) is a diagram based business process modelling language which merges IDEF3, RAD and PSL. It has a precise operational semantics and describes business processes in a conventional logic. Because it is diagram based, it can be easily accepted by processes modelers who normally are familiar with such diagrams. However, its problem is that it does not provide a direct specification for Web services based agents. This paper bridges the gap via conceptual mapping between two different process modelling languages, FBPML and OWL-S.

MOTIVATION: The field of 'DNA linguistics' has emerged from pioneering work in computational linguistics and molecular biology. Most formal grammars in this field are expressed using Definite Clause Grammars but these have computational limitations which must be overcome. The present study provides a new DNA parsing system, comprising a logic grammar formalism called Basic Gene Grammars and a bidirectional chart parser DNA-ChartParser. RESULTS: The use of Basic Gene Grammars is demonstrated in representing many formulations of the knowledge of Escherichia coli promoters, including knowledge acquired from human experts, consensus sequences, statistics (weight matrices), symbolic learning, and neural network learning. The DNA-ChartParser provides bidirectional parsing facilities for BGGs in handling overlapping categories, gap categories, approximate pattern matching, and constraints. Basic Gene Grammars and the DNA-ChartParser allowed different sources of knowledge for recognizing E.coli promoters to be combined to achieve better accuracy as assessed by parsing these DNA sequences in real-world data sets.

Business modelling methods are popular but, since they operate primarily in the early stages of software lifecycles, most are informal. This paper describes how we have used a conventional formal notation (first order predicate logic) in combination with automated support tools to replicate the key components of an established, informal, business modelling method: IBM's Business System Development Method (BSDM). We describe the knowledge which we represent formally at each stage in the method and explain how the move from informal to formal representation allows us to provide guidance and consistency checking during the development lifecycle of the model. It also allows us to extend the original method to a model execution phase which is not described in the original informal method. The role of the formal notation in this case is not to provide a formal semantics for BSDM but to provide a framework for sharing the information supplied at different modelling stages and which we can supplement with simple forms of automated analysis.

Requirements capture is a term used in software engineering, referring to the process of obtaining a problem description – a high level account of the problem which a user wants to solve. This description is then used to control the generation of a p

There have been several knowledge-based systems for statutory building regulations during the last decade, such as Fenves et al's systems using the SASE model, Stone and Wilcox's system using a rule-based approach, and Waard's system using Cornick et al;'s model-based approach. However, they take into account only one side of building regulations, considering them only in the context of design systems and ignoring the existence of case histories. Building regulations are also part of a legal system and have characteristics of law. In this paper, we propose a Knowledge-Intensive Case-based reasoning System which can be used for the retrieval and maintenance of building regulations and case histories. First, we propose a unified knowledge representation scheme for both statutory building regulations and case histories. Second, we describe the retrieval of regulations information, which uses the notion of implied similarity as well as structural mapping. Finally, we describe knowledge acquisition from case histories, which is guided by knowledge gained from statutory regulations and case histories.