One to one correspondences between entities are not always sufficient to describe the true relationship between related entities in diverse ontologies, and complex correspondences are needed instead. We demonstrate th...
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One to one correspondences between entities are not always sufficient to describe the true relationship between related entities in diverse ontologies, and complex correspondences are needed instead. We demonstrate the types of complex correspondence occurring between two LOD sources and compare techniques for discovering these complex correspondences.
Subgroup discovery and community detection are two approaches having been studied in different research areas like data mining and social network analysis. In this context, these techniques are especially helpful in o...
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Subgroup discovery and community detection are two approaches having been studied in different research areas like data mining and social network analysis. In this context, these techniques are especially helpful in order to provide for analytical and explorative data mining approaches. We present an organized picture of recent research in subgroup discovery and community detection specifically focusing on attributed graphs. That is, we include complex relational graphs that are annotated with additional information, e.g., attribute information on the nodes and/or edges of the graph. In addition, we especially summarize a method combining both community detection and subgroup discovery resulting in a description-oriented approach for.
Search user interfaces (SUIs) are usually designed and optimized for generic users or for a certain user group. Users within the group are similar, e.g. concerning their information need, search goals or cognitive ski...
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Persistent identification is necessary for recognition, dissemination and (external) cross-references to digital objects. Uniform Re-source Identifiers (URIs) provide an established scheme for this task, but do not gu...
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In social network analysis, there are a variety of options for investigating social interactions. This paper reviews our recent work on analyzing and grounding social interactions in online and offline networks consid...
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The localization industry currently deploys language translation workflows based on heterogeneous tool-chains. Standardized tool interchange formats such as XLIFF (XML Localization Interchange File Format) have had so...
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The localization industry currently deploys language translation workflows based on heterogeneous tool-chains. Standardized tool interchange formats such as XLIFF (XML Localization Interchange File Format) have had some impact on enabling more agile translation workflows. However the rise of new tools based on machine translation technology and the growing demand for enterprise linked data applications has created new interoperability challenges as workflows need to encompass a broader range of tools. In this paper we present an approach of representing mappings between RDF-based representations of multilingual content and meta-data. To represent the mappings, we use a combination of SPARQL Inferencing Notation (SPIN) and meta-data. Our approach allows the mapping representation to be published as Linked data. In contrast to other frameworks such as R2R, the mappings are executed via a standard SPARQL processor. The objective is to provide a more agile approach to translation workflows and greater interoperability between software tools by leveraging the ongoing innovation in the Multilingual Web field. Our use case is a Language Technology retraining workflow where publishing mappings leads to new opportunities for interoperability and end-to-end tool-chain analytics. We present the results from an initial experiment which compared our approach of executing and representing mappings to that of a similar approach - The R2R Framework.
In this paper, we explore alternative ways to visualize search results for children. We propose a novel search result visualization using characters. The main idea is to represent each web document as a character wher...
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The capability of building a model that can be understood and interpreted by humans is one of the main selling points of symbolic machine learning algorithms, such as rule or decision tree learners. However, those alg...
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The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate meth...
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The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system Bib-Sonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.
Common search engines deliver quite good results when the user has a precise notion of what he is looking for. However, the user might have in mind additional prior information regarding the importance of specific ter...
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