Artificial magnetic field induced by an evanescent wave

Cold atomic gases are perfect laboratories for realization of quantum simulators. In order to simulate solid state systems in the presence of magnetic fields special effort has to be made because atoms are charge neutral. There are different methods for realization of artificial magnetic fields, that is the creation of specific conditions so that the motion of neutral particles mimics the dynamics of charged particles in an effective magnetic field. Here, we consider adiabatic motion of atoms in the presence of an evanescent wave. Theoretical description of the adiabatic motion involves artificial vector and scalar potentials related to the Berry phases. Due to the large gradient of the evanescent field amplitude, the potentials can be strong enough to induce measurable effects in cold atomic gases. We show that the resulting artificial magnetic field is able to induce vortices in a Bose-Einstein condensate trapped close to a surface of a prism where the evanescent wave is created. We also analyze motion of an atomic cloud released from a magneto-optical trap that falls down on the surface of the prism. The artificial magnetic field is able to reflect falling atoms that can be observed experimentally.Comment: 19 pages, 7 figure

A cost model for ontology engineering

In this report we propose a methodology for cost estimation for ontologies and analyze cost factors implied in the engineering process. We examine the appropriateness of a COCOMO-like parametric approach to ontology cost estimation and propose a non-calibrated ontology cost model, which is to be continuously refined along with the collection of empiric data on person month efforts invested in developing ontologies in real-world projects. We further describe the human-driven evaluation of the cost drivers described in the parametric model on the basis of the cost models’ quality framework by Boehm[5

Ontology Engineering Cost Estimation with ONTOCOM

Techniques for reliably estimating development efforts are a fundamental requirement for a wide-scale dissemination of ontologies in business contexts. In this report we account for the similarities and differences between software and ontology engineering in order to establish the appropriateness of applying software cost models to ontologies. We present a parametric approach to cost estimation for ontology development – ONTOCOM – and analyze various cost factors implied in the ontology engineering process

Description of alignment evaluation and benchmarking results

shvaiko2007aNo abstract available

Methodik zum Finden von passenden Ontology-Matching-Verfahren

Interoperability has gained in importance and become an essential issue within the Semantic Web community. The more standardized and widespread the data manipulation tools are, the easier and more attractive using the Semantic Web approach has become. Though Semantic Web technologies can support the unambiguous identification of concepts and formally describe relationships between concepts, thereby allowing the representation of data in a more meaningful and more machine-understandable way, Web developers are still faced with the problem of semantic interoperability, which stands in the way of achieving the Web’s full potential. To attain semantic interoperability, systems must be capable of exchanging data in such a way that the precise meaning of the data is readily accessible, and the data itself can be translated by any system into a form that it understands. Hence, a central problem of interoperability and data integration issues in Semantic Web vision is schema or ontology matching and mapping. Considering this situation in SemanticWeb research, we wish to contribute to the enhancement of (semantic) interoperability by contributing to the ontology matching solution. The number of use cases for ontology matching justifies the great importanceof this topic in the Semantic Web. Furthermore, the development and existence of tried and tested ontology matching algorithms and support tools will be one of the crucial issues that may have a significant impact on future development. Therefore, we have developed a Metadata-based Ontology Matching (MOMA) Framework that addresses data integration and the interoperability issue by creating and maintaining awareness of the link between matching algorithms and various ontologies. Our approach allows for a more flexible manual and (semi-)automatic deployment of matching algorithms, depending on the specific requirements of the application (e.g. suitability to certain types of input) to which the matchers are to be utilized. Since it is difficult to theoretically compare the existing approaches due to the fact that they are based on different techniques, a matcher characteristic that describes the different approaches on various levels of detail is needed. We have hence developed a Multilevel Characteristic for Matching Approaches (MCMA), which forms part of the MOMA Framework and has been utilized for the matcher selection. Taking into account the requirements of the successful deployment of semantic technologies regarding off-the-shelf and easy to use tools, the MOMA Framework should be capable of meeting the demands of different users: humans (Semantic Web experts and ontology matching lay users) and machines (e.g. service/matching providers). For human users, the process of choosing the suitable approach can be carried out manually, while machines require at the very least a semi-automatic selection of appropriate matchers. In manual selection, since the decision depends on multiple criteria (MCMA) and scales are not consistent, we have applied a systematic approach that structures the expectation, intuition, and heuristic-based decision making into a well- defined methodology called Analytic Hierarchy Process (AHP). In order to (semi-) automatically determine which matchers are appropriate for a given application, the MOMA Framework uses additional information on the ontologies (ontology metadata) and available matchers (matcher metadata). The ontology metadata captures information about matching relevant ontology features while the matcher metadata, based on the MCMA, describes the most important characteristics of the matching services. Furthermore, since explicit knowledge about the dependencies between thematching algorithms and the structures on which they operate is needed, we have formalized it into dependency rules statements that, taking into account the characteristic of matching approaches and ontological sources to be matched, determine which elements (i.e. matchers) are to be used for a given set of ontologies. Since the evaluation aspects of the MOMA Framework are directly related to the usage of the framework in real-world situations, the evaluation of both the AHP and rule-based approaches has been conducted on real-world test cases defined by the Ontology Alignment Evaluation Initiative (OAEI) Campaign. The results of the evaluation process demonstrate the applicability of the MOMA Framework for matcher selection and the accuracy of its predictions. With theMOMAFramework, which allows for the selection of suitable matching approaches w.r.t the given application requirements, we intend to contribute to the tackling of real world challenges, which are commonly agreed testbeds and benchmarking, with the aim of ensuring seamless interoperability and integration of the various Semantic Web technologies.Interoperabilität gewinnt immer mehr an Bedeutung und spielt eine wichtige Rolle in der SemanticWeb Community. Obwohl semantische Technologien eine eindeutige Identifikation von Konzepten, sowie die formale Beschreibung der Verbindungen zwischen den Konzepten und dadurch eine bedeutungsvolle und maschinenverständliche Darstellung von Daten erlauben, werden Entwickler heutzutage leider immer noch mit dem Problem semantischer Interoperabilität konfrontiert, das als wichtiger Baustein zum Erreichen des vollen Potentials des Web anzusehen ist. Um semantische Interoperabilität erreichen zu können, müssen verschiedene Systeme in der Lage sein, die Daten so auszutauschen, dass deren genaue Bedeutung leicht zugänglich ist und sie in das Format übersetzt werden können, welches das entsprechende System versteht. Demzufolge spiegeln die Schema oder Ontologie Matching-(Vergleichs-) und Mapping-(Abbildungs-)Verfahren das zentrale Problem bzgl. der Dateninteroperabilität und -integration im Semantic Web wider. Angesichts dieser Situation, und weil die Entwicklung und Existenz von bereits getesteten und bewährten Ontologie-Matching-Algorithmen (Matcher) und Tools entscheidend für die zukünftige Weiterentwicklung und Ausbreitung von semantischen Technologien sein wird, wollen wir zu den Lösungen in diesem Bereich beisteuern. Wir haben ein Metadata-based Ontology Matching Framework (MOMA Framework) entwickelt, das zur Datenintegration und -interoperabilität beiträgt, indem es eine Verbindung zwischen Matchern und Ontologien schafft, um schließlich die passenden Verfahren für die betroffenen Ontologien in Abhängigkeit zu spezifischen Anwendungsanforderungen, in denen diese verwendet werden sollten, vorzuschlagen. Da es schwierig ist die existierenden Matchingverfahren auf einer theoretischen Basis miteinander zu vergleichen, wurde eine mehrstufige Charakteristik für Matchingverfahren (MCMA – Multilevel Characteristic for Matching Approaches) entwickelt. Sie beschreibt die verschiedenen Ansätze auf unterschiedlichen Detaillierungsniveaus und wird zur Matcherauswahl verwendet. Unter Berücksichtigung der Anforderungen für die erfolgreiche Anwendung der semantischen Technologien soll das MOMA Framework in der Lage sein, die Bedürfnisse und Ansprüche verschiedener Nutzergruppen – Menschen und Maschinen – zu bedienen. Für menschliche Benutzer kann der Auswahlprozess der passenden Ontologie-Matching Algorithmen manuell durchgeführt werden, während Maschinen zumindest eine semi-automatische Matcherauswahl erfordern. Da die Entscheidung über die Angemessenheit der Algorithmen von mehreren Kriterien abhängig ist und Vergleichsskalen nicht konsistent sind, wurde in dem manuellen Auswahlprozess ein systematisches Verfahren (analytischer Hierarchien-Prozess, AHP), das die Erwartung, die Intuition und die heuristische Entscheidungsfindung strukturiert, eingesetzt. Um die semi-automatische Auswahl der passenden Algorithmen zu ermöglichen benutzt das MOMA Framework zusätzliche Informationen – Metadaten – über Ontologien und vorhandene Matcher. Die Ontologie-Metadaten beinhalten Informationen über Ontologie-Eigenschaften, die eine entscheidende Rolle bei der Matcherauswahl spielen und die Matcher-Metadaten, die auf MCMA basieren, beschreiben die wichtigsten Eigenschaften der Matchingverfahren. Darüber hinaus, da explizites Wissen über die Abhängigkeiten zwischen den Matching Algorithmen und den Strukturen, auf denen die Matcher angewandt werden können, benötigt wird, haben wir das Wissen in Form von Abhängigkeitsregeln unter Betrachtung der Matchercharakteristik und den ontologischen Strukturen, die verglichen werden sollten, notiert und legten damit fest, welche Matcher für welche ontologische Quellen eingesetzt werden können. Da die Evaluationsaspekte von MOMA Framework direkt mit dem Einsatz des Frameworks in realen Situationen zusammenhängen, wurde sowohl die Evaluation vom AHP- basierten als auch vom regel-basiertem Ansatz im Kontext von Anwendungsfällen aus dem Wettbewerb der Ontology Alignement Evaluation Initiative (OAEI) durchgeführt. Die Ergebnisse des Evaluationsprozesses bestätigen die Anwendbarkeit des MOMA Frameworks und zeigen die Richtigkeit seiner Auswahlprognosen

Discourse Support Design Patterns

The aim of this work is to come up with a set of design patterns for discourse support systems. On the basis of the analysis carried out on various systems, the subsequent diagrams developed may be used for the development of discourse support systems

A Metadata-Based Generic Matching Framework for Web Ontologies

Current algorithms can not to be used optimally in automatic and semi-automatic ontology matching tasks as those envisioned by the Semantic Web community, mainly because of the inherent dependency between particular algorithms and ontology properties such as size, representation language or underlying graph structure, and because of performance and scalability limitations. In order to cope with the first problem we designed a generic matching framework which exploits the valuable ideas embedded in current matching approaches, but in the same time accounts for their limitations—for specific input ontologies it optimizes the matching results by automatically eliminating unsuitable candidate matching methods

Applying an analytic method for matching approach selection

Abstract. One of the main open issues in the ontology matching field is the selection of a current relevant and suitable matcher. The suitability of the given approaches is determined w.r.t the requirements of the application and with careful consideration of a number of factors. This work proposes a multilevel characteristic for matching approaches, which provides a basis for the comparison of different matchers and is used in the decision making process for selection the most appropriate algorithm.