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Institut
- Institute for Web Science and Technologies (50) (entfernen)
Ontologies play an important role in knowledge representation for sharing information and collaboratively developing knowledge bases. They are changed, adapted and reused in different applications and domains resulting in multiple versions of an ontology. The comparison of different versions and the analysis of changes at a higher level of abstraction may be insightful to understand the changes that were applied to an ontology. While there is existing work on detecting (syntactical) differences and changes in ontologies, there is still a need in analyzing ontology changes at a higher level of abstraction like ontology evolution or refactoring pattern. In our approach we start from a classification of model refactoring patterns found in software engineering for identifying such refactoring patterns in OWL ontologies using DL reasoning to recognize these patterns.
With the Multimedia Metadata Ontology (M3O), we have developed a sophisticated model for representing among others the annotation, decomposition, and provenance of multimedia metadata. The goal of the M3O is to integrate the existing metadata standards and metadata formats rather than replacing them. To this end, the M3O provides a scaffold needed to represent multimedia metadata. Being an abstract model for multimedia metadata, it is not straightforward how to use and specialize the M3O for concrete application requirements and existing metadata formats and metadata standards. In this paper, we present a step-by-step alignment method describing how to integrate and leverage existing multimedia metadata standards and metadata formats in the M3O in order to use them in a concrete application. We demonstrate our approach by integrating three existing metadata models: the Core Ontology on Multimedia (COMM), which is a formalization of the multimedia metadata standard MPEG-7, the Ontology for Media Resource of the W3C, and the widely known industry standard EXIF for image metadata
Existing tools for generating application programming interfaces (APIs) for ontologies lack sophisticated support for mapping the logics-based concepts of the ontology to an appropriate object-oriented implementation of the API. Such a mapping has to overcome the fundamental differences between the semantics described in the ontology and the pragmatics, i.e., structure, functionalities, and behavior implemented in the API. Typically, concepts from the ontology are mapped one-to-one to classes in the targeted programming language. Such a mapping only produces concept representations but not an API at the desired level of granularity expected by an application developer. We present a Model-Driven Engineering (MDE) process to generate customized APIs for ontologies. This API generation is based on the semantics defined in the ontology but also leverages additional information the ontology provides. This can be the inheritance structure of the ontology concepts, the scope of relevance of an ontology concept, or design patterns defined in the ontology.
In recent development, attempts have been made to integrate UML and OWL into one hybrid modeling language, namely TwoUse. This aims at making use of the benefits of both modeling languages and overcoming the restrictions of each. In order to create a modeling language that will actually be used in software development an integration with OCL is needed. This integration has already been described at the contextual level in, however an implementation is lacking so far. The scope of this paper is the programatical implementation of the integration of TwoUse with OCL. In order to achieve this, two different OCL implementations that already provide parsing and interpretation functionalities for expressions over regular UML. This paper presents two attempts to extend existing OCL implementations, as well as a comparison of the existing approaches.
The novel mobile application csxPOI (short for: collaborative, semantic, and context-aware points-of-interest) enables its users to collaboratively create, share, and modify semantic points of interest (POI). Semantic POIs describe geographic places with explicit semantic properties of a collaboratively created ontology. As the ontology includes multiple subclassiffcations and instantiations and as it links to DBpedia, the richness of annotation goes far beyond mere textual annotations such as tags. With the intuitive interface of csxPOI, users can easily create, delete, and modify their POIs and those shared by others. Thereby, the users adapt the structure of the ontology underlying the semantic annotations of the POIs. Data mining techniques are employed to cluster and thus improve the quality of the collaboratively created POIs. The semantic POIs and collaborative POI ontology are published as Linked Open Data.
We propose a new approach for mobile visualization and interaction of temporal information by integrating support for time with today's most prevalent visualization of spatial information, the map. Our approach allows for an easy and precise selection of the time that is of interest and provides immediate feedback to the users when interacting with it. It has been developed in an evolutionary process gaining formative feedback from end users.
In recent years ontologies have become common on the WWW to provide high-level descriptions of specific domains. These descriptions could be effectively used to build applications with the ability to find implicit consequences of their represented knowledge. The W3C developed the Resource Description Framework RDF, a language to describe the semantics of the data on the web, and the Ontology Web Language OWL, a family of knowledge representation languages for authoring ontologies. In this thesis we propose an ontology API engineering framework that makes use of the state-of-the-art ontology modeling technologies as well as of software engineering technologies. This system simplifies the design and implementation process of developing dedicated APIs for ontologies. Developers of semantic web applications usually face the problem of mapping entities or complex relations described in the ontology to object-oriented representations. Mapping complex relationship structures that come with complex ontologies to a useful API requires more complicated API representations than does the mere mapping of concepts to classes. The implementation of correct object persistence functions in such class representations also becomes quite complex.
Unlocking the semantics of multimedia presentations in the web with the multimedia metadata ontology
(2010)
The semantics of rich multimedia presentations in the web such as SMIL, SVG and Flash cannot or only to a very limited extend be understood by search engines today. This hampers the retrieval of such presentations and makes their archival and management a difficult task. Existing metadata models and metadata standards are either conceptually too narrow, focus on a specific media type only, cannot be used and combined together, or are not practically applicable for the semantic description of rich multimedia presentations. In this paper, we propose the Multimedia Metadata Ontology (M3O) for annotating rich, structured multimedia presentations. The M3O provides a generic modeling framework for representing sophisticated multimedia metadata. It allows for integrating the features provided by the existing metadata models and metadata standards. Our approach bases on Semantic Web technologies and can be easily integrated with multimedia formats such as the W3C standards SMIL and SVG. With the M3O, we unlock the semantics of rich multimedia presentations in the web by making the semantics machine-readable and machine-understandable. The M3O is used with our SemanticMM4U framework for the multi-channel generation of semantically-rich multimedia presentations.
Expert-driven business process management is an established means for improving efficiency of organizational knowledge work. Implicit procedural knowledge in the organization is made explicit by defining processes. This approach is not applicable to individual knowledge work due to its high complexity and variability. However, without explicitly described processes there is no analysis and efficient communication of best practices of individual knowledge work within the organization. In addition, the activities of the individual knowledge work cannot be synchronized with the activities in the organizational knowledge work.rnrnSolution to this problem is the semantic integration of individual knowledgernwork and organizational knowledge work by means of the patternbased core ontology strukt. The ontology allows for defining and managing the dynamic tasks of individual knowledge work in a formal way and to synchronize them with organizational business processes. Using the strukt ontology, we have implemented a prototype application for knowledge workers and have evaluated it at the use case of an architectural fifirm conducting construction projects.
Graphen sind eine gute Wahl um strukturierte Daten zu repräsentieren. TGraphen (typisierte, attributierte, geordnete und gerichtete Graphen) sind eine sehr generische Graphenart, die in vielen Bereichen verwendet werden können. Das Java Graphenlabor (JGraLab) bietet eine effiziente Implementierung von TGraphen mit all ihren Eigenschaften. Zusätzlich stellt es, unter anderem, die Anfragesprache GReQL2 zur Verfügung, die dazu verwendet werden kann, Daten aus einem Graphen zu extrahieren. Es verfügt jedoch nicht über eine generische Bibliothek von gängigen Graphalgorithmen. Diese Studienarbeit ergänzt JGraLab durch eine generische Algorithmenbibliothek namens Algolib, die eine generische und erweiterbare Implementierung einiger wichtiger gängiger Graphalgorithmen enthält. Das Hauptaugenmerk dieser Arbeit liegt auf der Generizität von Algolib, ihrer Erweiterbarkeit und der Methoden der Softwaretechnik die benutzt wurden um beides zu erreichen. Algolib ist auf zwei Weisen erweiterbar. Bereits enthaltene Algorithmen können erweitert werden um speziellere Probleme zu lösen und weitere Algorithmen können auf einfache Weise der Bibliothek hinzugefügt werden.
In dieser Doktorarbeit beschreibe ich das spektrale Verhalten von großen, dynamischen Netzwerken und formuliere das spektrale Evolutionsmodell. Das spektrale Evolutionsmodell beschreibt das Wachstum von Netzwerken, die sich im Laufe der Zeit ändern, und charakterisiert ihre Eigenwert-und Singulärwertzerlegung. Das spektrale Evolutionsmodell sagt aus, dass im Laufe der Zeit die Eigenwerte eines Netzwerks wachsen, und die Eigenvektoren nahezu konstant bleiben. Ich validiere das spektrale Evolutionsmodell empirisch mit Hilfe von über einhundert Netzwerkdatensätzen, und theoretisch indem ich zeige,dass es eine gewisse Anzahl von bekannten Algorithmen zur Kantenvorhersage verallgemeinert, darunter Graph-Kernel, Pfad-Zähl-Methoden, Rangreduktion und Triangle-Closing.
Die Sammlung von Datensätzen, die ich verwende enthält 118 distinkte Datensätze. Ein Datensatz, das soziale Netzwerk mit negativen Kanten des Slashdot-Zoo, wurde speziell während des Verfassens dieser Arbeit extrahiert. Ich zeige auch, dass das spektrale Evolutionsmodell als Generalisierung des Preferential-Attachment-Modells verstanden werden kann, wenn Wachstum in latenten Dimensionen einzeln betrachtet wird. Als Anwendungen des spektralen Evolutionsmodells führe ich zwei neue Algorithmen zur Kantenvorhersage ein, die in Empfehlungssystemen, Suchmaschinen, im Collaborative-Filtering, für die Vorhersage von Bewertungen, für die Vorhersage von Kantenvorzeichen und mehr verwendet werden können. Der erste Kantenvorhersagealgorithmus ergibt ein eindimensionales Curve-Fitting-Problem, aus dem eine spektrale Transformation gelernt wird. Die zweite Methode verwendet Extrapolation von Eigenwerten, um zukünftige Eigenwerte vorherzusagen. Als Spezialfälle zeige ich, dass das spektrale Evolutionsmodell auf gerichtete, ungerichtete, gewichtete, ungewichtete, vorzeichenbehaftete und bipartite Graphen erweitert werden kann. Für vorzeichenbehaftete Graphen führe ich neue Anwendungen der Laplace-Matrix zur Graphzeichnung, zur spektralen Clusteranalyse, und beschreibe neue Laplace-Graph-Kernel, die auf vorzeichenbehaftete Graphen angewendet werden können.
Ich definiere dazu den algebraischen Konflikt, ein Maß für den Konflikt, der in einem vorzeichenbehafteten Graphen vorhanden ist, und das auf der vorzeichenbehafteten Laplace-Matrix begründet ist. Ich beschreibe das Problem der Vorhersage von Kantenvorzeichen spektral, und führe die vorzeichenbehaftete Widerstands-Distanz ein. Für bipartite und gerichtete Graphen führe ich den Sinus-Hyperbolicus-und ungeraden Neumann-Kernel ein, welche den Exponential- und den Neumann-Kernel für ungerichtete unipartite Graphen verallgemeinern. Ich zeige zudem, dass das Problem der gerichteten und bipartiten Kantenvorhersage verwandt sind, dadurch dass beide durch die Evolution der Singulärwertzerlegung gelöst werden können.
We present the user-centered, iterative design of Mobile Facets, a mobile application for the faceted search and exploration of a large, multi-dimensional data set of social media on a touchscreen mobile phone. Mobile Facets provides retrieval of resources such as places, persons, organizations, and events from an integration of different open social media sources and professional content sources, namely Wikipedia, Eventful, Upcoming, geo-located Flickr photos, and GeoNames. The data is queried live from the data sources. Thus, in contrast to other approaches we do not know in advance the number and type of facets and data items the Mobile Facets application receives in a specific contextual situation. While developingrnMobile Facets, we have continuously evaluated it with a small group of fifive users. We have conducted a task-based, formative evaluation of the fifinal prototype with 12 subjects to show the applicability and usability of our approach for faceted search and exploration on a touchscreen mobile phone.
The Multimedia Metadata Ontology (M3O) provides a generic modeling framework for representing multimedia metadata. It has been designed based on an analysis of existing metadata standards and metadata formats. The M3O abstracts from the existing metadata standards and formats and provides generic modeling solutions for annotations, decompositions, and provenance of metadata. Being a generic modeling framework, the M3O aims at integrating the existing metadata standards and metadata formats rather than replacing them. This is in particular useful as today's multimedia applications often need to combine and use more than one existing metadata standard or metadata format at the same time. However, applying and specializing the abstract and powerful M3O modeling framework in concrete application domains and integrating it with existing metadata formats and metadata standards is not always straightforward. Thus, we have developed a step-by-step alignment method that describes how to integrate existing multimedia metadata standards and metadata formats with the M3O in order to use them in a concrete application. We demonstrate our alignment method by integrating seven different existing metadata standards and metadata formats with the M3O and describe the experiences made during the integration process.
In this paper, we compare two approaches for exploring large,rnhierarchical data spaces of social media data on mobile devicesrnusing facets. While the first approach arranges thernfacets in a 3x3 grid, the second approach makes use of arnscrollable list of facets for exploring the data. We have conductedrna between-group experiment of the two approachesrnwith 24 subjects (20 male, 4 female) executing the same set ofrntasks of typical mobile users" information needs. The resultsrnshow that the grid-based approach requires significantly morernclicks, but subjects need less time for completing the tasks.rnFurthermore, it shows that the additional clicks do not hamperrnthe subjects" satisfaction. Thus, the results suggest thatrnthe grid-based approach is a better choice for faceted searchrnon touchscreen mobile devices. To the best of our knowledge,rnsuch a summative evaluation of different approaches for facetedrnsearch on mobile devices has not been done so far.
Schema information about resources in the Linked Open Data (LOD) cloud can be provided in a twofold way: it can be explicitly defined by attaching RDF types to the resources. Or it is provided implicitly via the definition of the resources´ properties.
In this paper, we analyze the correlation between the two sources of schema information. To this end, we have extracted schema information regarding the types and properties defined in two datasets of different size. One dataset is a LOD crawl from TimBL- FOAF profile (11 Mio. triple) and the second is an extract from the Billion Triples Challenge 2011 dataset (500 Mio. triple). We have conducted an in depth analysis and have computed various entropy measures as well as the mutual information encoded in this two manifestations of schema information.
Our analysis provides insights into the information encoded in the different schema characteristics. It shows that a schema based on either types or properties alone will capture only about 75% of the information contained in the data. From these observations, we derive conclusions about the design of future schemas for LOD.
Various best practices and principles guide an ontology engineer when modeling Linked Data. The choice of appropriate vocabularies is one essential aspect in the guidelines, as it leads to better interpretation, querying, and consumption of the data by Linked Data applications and users.
In this paper, we present the various types of support features for an ontology engineer to model a Linked Data dataset, discuss existing tools and services with respect to these support features, and propose LOVER: a novel approach to support the ontology engineer in modeling a Linked Data dataset. We demonstrate that none of the existing tools and services incorporate all types of supporting features and illustrate the concept of LOVER, which supports the engineer by recommending appropriate classes and properties from existing and actively used vocabularies. Hereby, the recommendations are made on the basis of an iterative multimodal search. LOVER uses different, orthogonal information sources for finding terms, e.g. based on a best string match or schema information on other datasets published in the Linked Open Data cloud. We describe LOVER's recommendation mechanism in general and illustrate it alongrna real-life example from the social sciences domain.
Tagging-Systeme sind faszinierende dynamische Systeme in denen Benutzer kollaborativ Ressourcen mit sogenannten Tags indexieren. Um das volle Potential von Tagging-Systemen nutzen zu können ist es wichtig zu verstehen, wie sich das Verhalten der einzelnen Benutzer auf die Eigenschaften des Gesamtsystems auswirkt. In der vorliegenden Arbeit wird das Epistemic Dynamic Model präsentiert. Es schlägt eine Brücke zwischen dem Benutzerverhalten und den Systemeigenschaften. Das Modell basiert auf der Annahme, dass der Einfluss des gemeinsamen Hintergrundwissens der Benutzer und der Imitation von Tag-Vorschlägen ausreicht, um die Entstehung der Häufigkeitsverteilungen der Tags und des Wachstums des Vokabulars zu erklären. Diese beiden Eigenschaften eines Tagging-Systems hängen eng mit der Entstehung eines gemeinsamen Vokabulars der Benutzer zusammen. Mit Hilfe des Epistemic Dynamic Models zeigen wir, dass die generelle Ausprägung der Tag-Häufigkeitsverteilungen und des Wachstums des Vokabulars ihren Ursprung in dem gemeinsamen Hintergrundwissen der Benutzer haben. Tag-Vorschläge können dann dazu genutzt werden, um gezielt diese generelle Ausprägung zu beeinflussen. In der vorliegenden Arbeit untersuchen wir hauptsächlich den Einfluss der von Vorschlägen populärer Tags ausgeht. Populäre Tags sorgen für einen Feedback-Mechanismus zwischen den Vokabularen der einzelnen Benutzer, der die Inter-Indexer Konsistenz der Tag-Zuweisungen erhöht. Wie wird aber dadurch die Indexierungsqualität in Tagging-Systemen beeinflusst? Zur Klärung dieser Frage untersuchen wir eine Methode zur Messung der Inter-Ressourcen Konsistenz der Tag-Zuweisungen. Die Inter-Ressourcen Konsistenz korreliert positiv mit der Indexierungsqualität, und mit der Trefferquote und der Genauigkeit von Suchanfragen an das System. Sie misst inwieweit die Tag-Vektoren die durch Benutzer wahrgenommene Ähnlichkeit der jeweiligen Ressourcen widerspiegeln. Wir legen mit Hilfe unseres Modell dar, und zeigen es auch mit Hilfe eines Benutzerexperiments, dass populäre Tags zu einer verringerten Inter-Ressourcen Konsistenz führen. Des Weiteren zeigen wir, dass die Inter-Ressourcen Konsistenz erhöht wird, wenn dem Benutzer das eigene, bisher genutzte Vokabular vorgeschlagen wird. Unsere Methode zur Messung der Inter-Ressourcen Konsistenz ergänzt bestehende Evaluationsmaße für Tag-Vorschlags-Algorithmen um den Aspekt der Indexierungsqualität.
The way information is presented to users in online community platforms has an influence on the way the users create new information. This is the case, for instance, in question-answering fora, crowdsourcing platforms or other social computation settings. To better understand the effects of presentation policies on user activity, we introduce a generative model of user behaviour in this paper. Running simulations based on this user behaviour we demonstrate the ability of the model to evoke macro phenomena comparable to the ones observed on real world data.
Modeling and publishing Linked Open Data (LOD) involves the choice of which vocabulary to use. This choice is far from trivial and poses a challenge to a Linked Data engineer. It covers the search for appropriate vocabulary terms, making decisions regarding the number of vocabularies to consider in the design process, as well as the way of selecting and combining vocabularies. Until today, there is no study that investigates the different strategies of reusing vocabularies for LOD modeling and publishing. In this paper, we present the results of a survey with 79 participants that examines the most preferred vocabulary reuse strategies of LOD modeling. Participants of our survey are LOD publishers and practitioners. Their task was to assess different vocabulary reuse strategies and explain their ranking decision. We found significant differences between the modeling strategies that range from reusing popular vocabularies, minimizing the number of vocabularies, and staying within one domain vocabulary. A very interesting insight is that the popularity in the meaning of how frequent a vocabulary is used in a data source is more important than how often individual classes and properties arernused in the LOD cloud. Overall, the results of this survey help in understanding the strategies how data engineers reuse vocabularies, and theyrnmay also be used to develop future vocabulary engineering tools.
Next Word Prediction beschreibt die Aufgabe, das Wort vorzuschlagen, welches ein Nutzer mit der höchsten Wahrscheinlichkeit als Nächstes eingeben wird. Momentane Ansätze basieren auf der Analyse sogenannter Corpora (große Textdateien) durch empirischen Methoden. Die resultierende Wahrscheinlichkeitsverteilungen über die vorkommenden Wortsequenzen werden als Language Models bezeichnet und zur Vorhersage des wahrscheinlichsten Wortes genutzt. Verbreitete Language Models basieren auf n-gram Sequenzen und Smoohting Algorithmen wie beispielsweise dem modifizierten Kneser-Ney Smoothing zur Anpassung der Wahrscheinlichkeit von ungesehenen Sequenzen. Vorherige Untersuchungen haben gezeigt, dass das Einfügen von Platzhaltern in solche n-gram Sequenzen zu besseren Ergebnissen führen kann, da dadurch die Berechnung von seltenen und ungesehenen Sequenzen weiter verbessert wird. Das Ziel dieser Arbeit ist die Formalisierung und Implementierung dieses neuen Ansatzes, wobei zusätzlich das modifizierte Kneser-Ney Smoothing eingesetzt werden soll.