51,587 research outputs found
Towards design patterns for ontology alignment
scharffe2008aInternational audienceAligning ontologies is a crucial and tedious task. Matching algorithms and tools provide support to facilitate the task of the user in defining correspondences between ontologies entities. However, automatic matching is actually limited to the detection of simple one to one correspondences to be further refined by the user. We introduce in this paper Correspondence Patterns as a tool to assist the design of ontology alignments. Based on existing research on patterns in the fields of software and ontology engineering, we define a pattern template and use it to develop a correspondence patterns library. This library is published in RDF following the Alignment Ontology vocabulary
Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web
Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions
Ontology-based patterns for the integration of business processes and enterprise application architectures
Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge
the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture
descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of
software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data.
Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an
ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their
applicability in business process-driven application integration is demonstrated
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
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An ontology to model the research process in information systems
The IS community has relied mostly on two main paradigms to undertake IS research: positivist and interpretivist. This paper argues that the ongoing debate around which of these paradigms is better suited to undertake IS research has created confusion amongst IS researchers, particularly between those who are relatively inexperienced (e.g. PhD researchers). Inexperienced researchers tend to place emphasis on the justification of their research approaches in the context of existing paradigms without offering a clear description of how the chosen methods and paradigms are applied in the context of their own research, a key issue to assess and understand any research output. This paper does not attempt to give any suggestions as to which research methods/paradigms should be used for IS research, but to raise the awareness that the way we currently communicate our thoughts in the research methods domain may not be very effective. We argue that an initial step to undertake this challenge could be to take a more âpracticalâ approach by focusing on the process of thinking and planning the research activity rather than focusing on the justification of the use of one or many research methods usually âloanedâ from other discipline
MultiFarm: A benchmark for multilingual ontology matching
In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual
ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different
languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages â Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish â we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism
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