Optimizing Ontology Alignments through NSGA-II without Using Reference Alignment

Ontology is widely used to solve the data heterogeneity problems on the semantic web, but the available ontologies could themselves introduce heterogeneity. In order to reconcile these ontologies to implement the semantic interoperability, we need to find the relationships among the entities in various ontologies, and the process of identifying them is called ontology alignment. In all the existing matching systems that use evolutionary approaches to optimize their parameters, a reference alignment between two ontologies to be aligned should be given in advance which could be very expensive to obtain especially when the scale of ontologies is considerably large. To address this issue, in this paper we propose a novel approach to utilize the NSGA-II to optimize the ontology alignments without using the reference alignment. In our approach, an adaptive aggregation strategy is presented to improve the efficiency of optimizing process and two approximate evaluation measures, namely match coverage and match ratio, are introduced to replace the classic recall and precision on reference alignment to evaluate the quality of the alignments. Experimental results show that our approach is effective and can find the solutions that are very close to those obtained by the approaches using reference alignment, and the quality of alignments is in general better than that of state of the art ontology matching systems such as GOAL and SAMBO

A Web/Grid Services Approach for Integration of Virtual Clinical & Research Environments

Clinicans have responsibilities for audit and research, often participating in projects with basic scientist colleagues. Our work in a regional teaching hospital setting involves collaboration with the medical school computer services and builds upon work developed in computer science department as part of the Collaborative Orthopaedic Research Environment (CORE) project[1]. This has established a pilot study for proof of concept work. Users are mapped to a personal profile implemented using XML and a service oriented architecture (SOA)[2,3]. This bridges the e-Health and e-Science domains, addressing some of the basic questions of security and uptake

Complexity and Philosophy

The science of complexity is based on a new way of thinking that stands in sharp contrast to the philosophy underlying Newtonian science, which is based on reductionism, determinism, and objective knowledge. This paper reviews the historical development of this new world view, focusing on its philosophical foundations. Determinism was challenged by quantum mechanics and chaos theory. Systems theory replaced reductionism by a scientifically based holism. Cybernetics and postmodern social science showed that knowledge is intrinsically subjective. These developments are being integrated under the header of “complexity science”. Its central paradigm is the multi-agent system. Agents are intrinsically subjective and uncertain about their environment and future, but out of their local interactions, a global organization emerges. Although different philosophers, and in particular the postmodernists, have voiced similar ideas, the paradigm of complexity still needs to be fully assimilated by philosophy. This will throw a new light on old philosophical issues such as relativism, ethics and the role of the subject

The ecology of wisdom

This is the first of two papers concerning wisdom as an ecosystem appearing in sequential editions of Management & Marketing journal. The notion of wisdom as an ecosystem, or “the wisdom ecology,” builds on work by Hays (2007) who first identified wisdom as an organisational construct and proposed a dynamic model of it. The centrepiece of this paper and the companion part to follow is a relationship map of the wisdom ecosystem (the Causal Loop Diagram at Figure 1). This first instalment provides background on wisdom and complex adaptive systems, and introduces the wisdom ecosystem model. The second instalment, “Mapping Wisdom as a Complex Adaptive System,” appearing in the next edition of Management & Marketing, explains systems dynamics modelling and discusses the wisdom ecosystem model in detail. It covers the four domains, or subsystems, of the wisdom ecosystem, Dialogue, Communal Mind, Collective Intelligence, and Wisdom, and walks readers through the model, exploring each of its 24 elements in turn. That second paper examines the relationships amongst system elements and illuminates important aspects of systems function.causal loop diagramming, complexity, dialogue, organisational learning, systems dynamics, wisdom.

state of the art analysis ; working packages in project phase II

In this report, we introduce our goals and present our requirement analysis for the second phase of the Corporate Semantic Web project. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge

ONTOLOGY MAPPING: TOWARDS SEMANTIC INTEROPERABILITY IN DISTRIBUTED AND HETEROGENEOUS ENVIRONMENTS

The World Wide Web (WWW) now is widely used as a universal medium for information exchange. Semantic interoperability among different information systems in the WWW is limited due to information heterogeneity, and the non semantic nature of HTML and URLs. Ontologies have been suggested as a way to solve the problem of information heterogeneity by providing formal, explicit definitions of data and reasoning ability over related concepts. Given that no universal ontology exists for the WWW, work has focused on finding semantic correspondences between similar elements of different ontologies, i.e., ontology mapping. Ontology mapping can be done either by hand or using automated tools. Manual mapping becomes impractical as the size and complexity of ontologies increases. Full or semi-automated mapping approaches have been examined by several research studies. Previous full or semi-automated mapping approaches include analyzing linguistic information of elements in ontologies, treating ontologies as structural graphs, applying heuristic rules and machine learning techniques, and using probabilistic and reasoning methods etc. In this paper, two generic ontology mapping approaches are proposed. One is the PRIOR+ approach, which utilizes both information retrieval and artificial intelligence techniques in the context of ontology mapping. The other is the non-instance learning based approach, which experimentally explores machine learning algorithms to solve ontology mapping problem without requesting any instance. The results of the PRIOR+ on different tests at OAEI ontology matching campaign 2007 are encouraging. The non-instance learning based approach has shown potential for solving ontology mapping problem on OAEI benchmark tests

Monitoring and Information Alignment in Pursuit of an IoT-Enabled Self-Sustainable Interoperability

To remain competitive with big corporations, small and medium-sized enterprises (SMEs) often need to be more dynamic, adapt to new business situations, react faster, and thereby survive in today‘s global economy. To do so, SMEs normally seek to create consortiums, thus gaining access to new and more opportunities. However, this strategy may also lead to complications. Due to the different sources of enterprise models and semantics, organizations are experiencing difficulties in seamlessly exchanging vital information via electronic means. In their attempt to address this issue, most seek to achieve interoperability by establishing peer-to-peer mappings with different business partners, or by using neutral data standards to regulate communications in optimized networks. Moreover, systems are more and more dynamic, frequently changing to answer new customer‘s requirements, causing new interoperability problems and a reduction of efficiency. Another situation that is constantly changing is the devices used in the enterprises, as the Enterprise Information Systems, devices are used to register internal data, and to be used to monitor several aspects. These devices are constantly changing, following the evolution and growth of the market. So, it is important to monitor these devices and doing a model representation of them. This dissertation proposes a self-sustainable interoperable framework to monitor existing enterprise information systems and their devices, monitor the device/enterprise network for changes and automatically detecting model changes. With this, network harmonization disruptions are detected in a timely way, and possible solutions are suggested to regain the interoperable status, thus enhancing robustness for reaching sustainability of business networks along time

How to Knit Your Own Markov Blanket

Hohwy (Hohwy 2016, Hohwy 2017) argues there is a tension between the free energy principle and leading depictions of mind as embodied, enactive, and extended (so-called ‘EEE1 cognition’). The tension is traced to the importance, in free energy formulations, of a conception of mind and agency that depends upon the presence of a ‘Markov blanket’ demarcating the agent from the surrounding world. In what follows I show that the Markov blanket considerations do not, in fact, lead to the kinds of tension that Hohwy depicts. On the contrary, they actively favour the EEE story. This is because the Markov property, as exemplified in biological agents, picks out neither a unique nor a stationary boundary. It is this multiplicity and mutability– rather than the absence of agent-environment boundaries as such - that EEE cognition celebrates