52 research outputs found

    Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

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    [EN] Knowledge engineering relies on ontologies, since they provide formal descriptions of real¿world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi¿automatically or automatically from scratch. It not only improves the efficiency of the ontology development pro¿ cess but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great poten¿ tial of ontology learning, we present an automatic ontology¿based model evolution approach to ac¿ count for highly dynamic environments at runtime. This approach can extend initial models ex¿ pressed as ontologies to cope with rapid changes encountered in surrounding dynamic environ¿ ments at runtime. The main contribution of our presented approach is that it analyzes heterogene¿ ous semi¿structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology¿based model. Within this approach, we aim to automatically evolve an initial ontology¿based model through the ontology learning approach. Therefore, this approach is illustrated using a proof¿of¿concept implementation that demonstrates the ontology¿based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology¿based models. First, we consider a feature¿based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria¿based eval¿ uation to assess the content of the evolved models. Finally, we perform an expert¿based evaluation to assess an initial and evolved models¿ coverage from an expert¿s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.Jabla, R.; Khemaja, M.; Buendía García, F.; Faiz, S. (2021). Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments. Applied Sciences. 11(22):1-30. https://doi.org/10.3390/app112210770130112

    Big Data in Management Research. Exploring New Avenues

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    Big Data in Management Research

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    Digital computers entered our homes, landed on our desktops, slipped into our pockets, and have seemingly become ubiquitous. At an ever faster pace, these devices have become highly interconnected and interoperable. Consequently, our archives, our work, our actions, and our interactions are increasingly digitalized and stored in databases or made accessible via the Internet. This data, generally characterized by high volume, variety, and velocity (i.e., accumulation rate), has come to be called “Big Data”. As of yet, Big Data has seldom been utilized in management research. Therefore, this dissertation explores the opportunities that Big Data brings for management scholars and describes three distinct projects that show how Big Data can be utilized in management research

    A descriptive type foundation for RDF Schema

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    This paper provides a type theoretic foundation for descriptive types that appear in Linked Data. Linked Data is data published on the Web according to principles and standards supported by the W3C. Such Linked Data is inherently messy: this is due to the fact that instead of being assigned a strict a priori schema, the schema is inferred a posteriori. Moreover, such a posteriori schema consists of opaque names that guide programmers, without prescribing structure. We employ what we call a descriptive type system for Linked Data. This descriptive type system differs from a traditional type system in that it provides hints or warnings rather than errors and evolves to describe the data while Linked Data is discovered at runtime. We explain how our descriptive type system allows RDF Schema inference mechanisms to be tightly coupled with domain specific scripting languages for Linked Data, enabling interactive feedback to Web developers.MOE (Min. of Education, S’pore)Accepted versio

    Big Data in Management Research. Exploring New Avenues

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    The language dura

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    Analyzing user feedback of on-line communities

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    The economic success of the World Wide Web makes it a highly competitive environment for web businesses. For this reason, it is crucial for web business owners to learn what their customers want. This thesis provides a conceptual framework and an implementation of a system that helps to better understand the behavior and potential interests of web site visitors by accounting for both explicit and implicit feedback. This thesis is divided into two parts. The first part is rooted in computer science and information systems and uses graph theory and an extended click-stream analysis to define a framework and a system tool that is useful for analyzing web user behavior by calculating the interests of the users. The second part is rooted in behavioral economics, mathematics, and psychology and is investigating influencing factors on different types of web user choices. In detail, a model for the cognitive process of rating products on the Web is defined and an importance hierarchy of the influencing factors is discovered. Both parts make use of techniques from a variety of research fields and, therefore, contribute to the area of Web Science.Welche Interessen verfolgen meine Webseiten-Nutzer? Diese Frage beschäftigt viele Betreiber von Online-Unternehmen. Um in einem solch hart umkämpften Markt wie dem des Internetbusiness erfolgreich bestehen zu können, ist es für die Entscheidungsträger dieser Unternehmen ausschlaggebend zu verstehen, welche Ziele ihre Kunden verfolgen. Hauptziel der vorliegenden Arbeit ist es, diese Frage mit Hilfe eines konzeptionellen Bezugssystems und der Implementierung eines Systems zu beantworten. Beide Elemente berücksichtigen sowohl das Verhalten, als auch das explizite und das implizite Feedback der Webseiten-Nutzer. Der vorgeschlagene Lösungsansatz unterstützt Betreiber von Online-Unternehmen dabei ihre Kunden besser zu verstehen. Dies geschieht durch das Beobachten und Auswerten des Kundenverhaltens, um daraus die vermuteten Kundeninteressen zu berechnen. Außerdem werden, um den Prozess des Feedbackgebens besser zu verstehen, diejenigen Faktoren untersucht, die die Auswahl des Webseiten-Nutzers beim Feedbackgeben beeinflussen. Folgende Forschungsfragen werden in dieser Arbeit im Hinblick auf unterschiedliche Aspekte des Feedbacks von Webseiten-Nutzern untersucht: * Was lernen wir aus der Analyse des explizit und des implizit durch die Webseiten-Nutzer ausgeführten Feedbacks? * Was sind die wichtigsten Faktoren, die das Feedback von Webseiten-Nutzern beeinflussen

    Ontology-based composition and matching for dynamic cloud service coordination

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    Recent cross-organisational software service offerings, such as cloud computing, create higher integration needs. In particular, services are combined through brokers and mediators, solutions to allow individual services to collaborate and their interaction to be coordinated are required. The need to address dynamic management - caused by cloud and on-demand environments - can be addressed through service coordination based on ontology-based composition and matching techniques. Our solution to composition and matching utilises a service coordination space that acts as a passive infrastructure for collaboration where users submit requests that are then selected and taken on by providers. We discuss the information models and the coordination principles of such a collaboration environment in terms of an ontology and its underlying description logics. We provide ontology-based solutions for structural composition of descriptions and matching between requested and provided services

    Data integration in mediated service compositions

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    A major aim of the Web service platform is the integration of existing software and information systems. Data integration is a central aspect in this con- text. Traditional techniques for information and data transformation are, however, not sucient to provide exible and automatable data integration solutions for Web and Cloud service-enabled information systems. The diculties arise from a high degree of complexity in data structures in many applications and from the additional problem of heterogenity of data representation in applications that often cross organisational boundaries. We present an integration technique that embeds a declarative data transformation technique based on semantic data models as a mediator service into a Web service-oriented information system architecture. Automation through consistency-oriented semantic data models and exibility through modular declarative data transformations are the key enablers of the approach. Automation is needed to enable dynamic integration and composition. Modiability is another aim here that benets from consistency and modularity

    Keyword-Based Querying for the Social Semantic Web

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    Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches
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