Enrico Minack

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This paper introduces the NEPOMUK project which aims to create a standard and reference implementation for the Social Semantic Desktop. We outline the requirements and functionalities that were identified for a useful Semantic Desktop system and present an architecture that fulfills these requirements which was acquired by incremental refinement of the(More)
In this paper we study the connection between sentiment of images expressed in metadata and their visual content in the social photo sharing environment Flickr. To this end, we consider the bag-of-visual words representation as well as the color distribution of images, and make use of the SentiWordNet thesaurus to extract numerical values for their(More)
Result diversification is an effective method to reduce the risk that none of the returned results satisfies a user's query intention. It has been shown to decrease query abandonment substantially. On the other hand, computing an optimally diverse set is NP-hard for the usual objectives. Existing greedy diversification algorithms require random access to(More)
Constructing semantic queries is a demanding task for human users, as it requires mastering a query language as well as the schema which has been used for storing the data. In this paper, we describe QUICK, a novel system for helping users to construct semantic queries in a given domain. QUICK combines the convenience of keyword search with the expressivity(More)
With the growth of the Web and the variety of search engine users, Web search e ectiveness and user satisfaction can be improved by diversi cation. This paper surveys recent approaches to search result diversi cation in both full-text and structured content search. We identify commonalities in the proposed methods describing an overall framework for result(More)
The rapidly increasing quantity and diversity of data stored on our PCs made locating information in this environment very difficult. Consequently, recent research has focussed on building semantically enhanced systems for either organizing or searching data on the desktop. Building on previous work, in this paper we present the Beagle toolbox, a set of(More)
With the growth of the Semantic Web, the requirements on storing and querying RDF has become more sophisticated. When a larger amount of data has to be managed, queries in structured query languages, such as SPARQL, are not always powerful enough. Use of additional keywords for querying can further reduce the result set towards the actual relevant answers,(More)
We present a brief overview of the way in which image analysis, coupled with associated collateral text, is being used for auto-annotation and sentiment analysis. In particular, we describe our approach to auto-annotation using the graphtheoretic dominant set clustering algorithm and the annotation of images with sentiment scores from SentiWordNet.(More)
Search on PCs has become less efficient than searching the Web due to the increasing amount of stored data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop search results. We also present a practical application of this(More)
More and more applications use the RDF framework as their data model and RDF stores to index and retrieve their data. Many of these applications require both structured queries as well as fulltext search. SPARQL addresses the first requirement in a standardized way, while fulltext search is provided by store-specific implementations. RDF benchmarks enable(More)