1,558 research outputs found
Git4Voc: Git-based Versioning for Collaborative Vocabulary Development
Collaborative vocabulary development in the context of data integration is
the process of finding consensus between the experts of the different systems
and domains. The complexity of this process is increased with the number of
involved people, the variety of the systems to be integrated and the dynamics
of their domain. In this paper we advocate that the realization of a powerful
version control system is the heart of the problem. Driven by this idea and the
success of Git in the context of software development, we investigate the
applicability of Git for collaborative vocabulary development. Even though
vocabulary development and software development have much more similarities
than differences there are still important differences. These need to be
considered within the development of a successful versioning and collaboration
system for vocabulary development. Therefore, this paper starts by presenting
the challenges we were faced with during the creation of vocabularies
collaboratively and discusses its distinction to software development. Based on
these insights we propose Git4Voc which comprises guidelines how Git can be
adopted to vocabulary development. Finally, we demonstrate how Git hooks can be
implemented to go beyond the plain functionality of Git by realizing
vocabulary-specific features like syntactic validation and semantic diffs
Ontology selection: ontology evaluation on the real Semantic Web
The increasing number of ontologies on the Web and the appearance of large scale ontology repositories has brought the topic of ontology selection in the focus of the semantic web research agenda. Our view is that ontology evaluation is core to ontology selection and that, because ontology selection is performed in an open Web environment, it brings new challenges to ontology evaluation.
Unfortunately, current research regards ontology selection and evaluation as two separate topics. Our goal in this paper is to explore how these two tasks relate. In particular, we are interested to get a better understanding of the ontology selection task and filter out the challenges that it brings to ontology evaluation. We discuss requirements posed by the open Web environment on ontology selection, we overview existing work on selection and point out future directions. Our major conclusion is that, even if selection methods still need further development, they have already brought novel approaches to ontology evaluatio
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Ontology summarization: an analysis and an evaluation
Ontology summarization has been recognized as a very useful technique to facilitate ontology understanding and then support ontology reuse as a new or supplementing technique. A number of efforts have emerged lately that apply different criteria, addressing different features of ontology, to extract ontology summaries. However, those efforts are ad-hoc in that there lacks consensus on a number of issues fundamental to the development of the field, such as a definition for ontology summarization, use case scenarios etc. Also, there lack sufficient evaluations and analysis, e.g. comparison among them and with other similar techniques, to provide meaning guidelines for users of this technique. With the aim to provide solutions to those fundamental issues, in this work, we present an analysis of this technique and its approaches. With the help of an objective evaluation method, we investigate what features of ontology are important in ontology summarization
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Modularization: a key for the dynamic selection of relevant knowledge components
Ontology selection is crucial to support knowledge reuse on the ever increasing Semantic Web. However, applications that rely on reusing existing knowledge often require only relevant parts of existing ontologies rather than entire ontologies. In this paper we investigate how modularization can be integrated with ontology selection techniques. Our contribution is twofold. On the one hand we extend a selection technique with a modularization component. On the other hand we design and implement a modularization algorithm which, unlike many existing approaches, is tightly integrated in a concrete tool
requirements and use cases
In this report, we introduce our initial vision of the Corporate Semantic Web
as the next step in the broad field of Semantic Web research. We identify
requirements of the corporate environment and gaps between current approaches
to tackle problems facing ontology engineering, semantic collaboration, and
semantic search. Each of these pillars will yield innovative methods and tools
during the project runtime until 2013. 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. We propose an initial layout
for an integrative architecture of a Corporate Semantic Web provided by these
three core pillars
prototypical implementations
In this technical report, we present prototypical implementations of
innovative tools and methods developed according to the working plan outlined
in Technical Report TR-B-09-05 [23]. We present an ontology modularization and
integration framework and the SVoNt server, the server-side end of an SVN-
based versioning system for ontologies in the Corporate Ontology Engineering
pillar. For the Corporate Semantic Collaboration pillar, we present the
prototypical implementation of a light-weight ontology editor for non-experts
and an ontology based expert finder system. For the Corporate Semantic Search
pillar, we present a prototype for algorithmic extraction of relations in
folksonomies, a tool for trend detection using a semantic analyzer, a tool for
automatic classification of web documents using Hidden Markov models, a
personalized semantic recommender for multimedia content, and a semantic
search assistant developed in co-operation with the Museumsportal Berlin. The
prototypes complete the next milestone on the path to an integral Cor- porate
Semantic Web architecture based on the three pillars Corporate Ontol- ogy
Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search,
as envisioned in [23]
PoN-S : a systematic approach for applying the Physics of Notation (PoN)
Visual Modeling Languages (VMLs) are important instruments of communication between modelers and stakeholders. Thus, it is important to provide guidelines for designing VMLs. The most widespread approach for analyzing and designing concrete syntaxes for VMLs is the so-called Physics of Notation (PoN). PoN has been successfully applied in the analysis of several VMLs. However, despite its popularity, the application of PoN principles for designing VMLs has been limited. This paper presents a systematic approach for applying PoN in the design of the concrete syntax of VMLs. We propose here a design process establishing activities to be performed, their connection to PoN principles, as well as criteria for grouping PoN principles that guide this process. Moreover, we present a case study in which a visual notation for representing Ontology Pattern Languages is designed
Relational contexts and conceptual model clustering
In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able to understand and reason with their content. In other words, it is important for these reference conceptual models to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages the rich semantics of ontology-driven conceptual models (ODCM). In particular, the technique employs the notion of Relational Context to guide automated model breakdown. Such Relational Contexts capture all the information needed for understanding entities “qua players of roles” in the scope of an objectified (reified) relationship (relator)
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