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16th Workshops on Learning, knowledge, Adaptation, LWA 2014: knowledge Discovery, data Mining and Machine Learning, KDML 2014, Information Retrieval, IR 2014 and knowledge Management, FGWM 2014

作者： Scholz, Christoph Illig, Jens Atzmueller, Martin Stumme, Gerd University of Kassel ITeG Research Center Knowledge and Data Engineering Group Wilhelmshöher Allee 73 Kassel Germany

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Evaluating assumptions about social tagging: A study of user behavior in BibSonomy 16

Evaluating assumptions about social tagging: A study of user...

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16th Workshops on Learning, knowledge, Adaptation, LWA 2014: knowledge Discovery, data Mining and Machine Learning, KDML 2014, Information Retrieval, IR 2014 and knowledge Management, FGWM 2014

作者： Doerfel, Stephan Zoller, Daniel Singer, Philipp Niebler, Thomas Hotho, Andreas Strohmaier, Markus ITeG and Knowledge and Data Engineering Group University of Kassel Germany Data Mining and Information Retrieval Group University of Würzburg Germany GESIS Germany University of Koblenz Germany

Social tagging systems have established themselves as an important part in today's web and have attracted the interest of our research community in a variety of investigations. Henceforth, several assumptions about social tagging systems have emerged on which our community also builds their work. Yet, testing such assumptions has been dificult due to the absence of suitable usage data in the past. In this work, we investigate and evaluate four assumptions about tagging systems by examining live server log data gathered from the public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected in a very critical light. Copyright © 2014 by the paper's authors.

关键词： Behavioral research

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Discovering implicational knowledge in wikidata

arXiv

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arXiv 2019年

作者： Hanika, Tom Marx, Maximilian Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Germany ITeG University of Kassel Germany TU Dresden Germany

knowledge graphs have recently become the state-of-The-Art tool for representing the diverse and complex knowledge of the world. Ex-amples include the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata. A distinguishing feature of Wikidata is that the knowledge is collaboratively edited and curated. While this greatly enhances the scope of Wikidata, it also makes it impossible for a single individual to grasp complex connections between properties or understand the global impact of edits in the graph. We apply Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data. Although the complex structure of data modelling inWikidata is not amenable to a direct approach, we overcome this limitation by extracting contextual representations of parts of Wiki-data in a systematic fashion. We demonstrate the practical feasibility of our approach through several experiments and show that the results may lead to the discovery of interesting implicational knowledge. Besides providing a method for obtaining large real-world data sets for FCA, we sketch potential applications in offering semantic assistance for editing and curating Wikidata.68T30 03G10 68T27 Copyright © 2019, The Authors. All rights reserved.

关键词： Formal concept analysis

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Mining social media to inform peatland fire and haze disaster management

arXiv

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arXiv 2017年

作者： Kibanov, Mark Stumme, Gerd Amin, Imaduddin Lee, Jong Gun Knowledge & Data Engineering Group ITeG Research Center University of Kassel Germany Pulse Lab Jakarta UN Global Pulse United Nations

Peatland fires and haze events are disasters with national, regional, and international implications. The phenomena lead to direct damage to local assets, as well as broader economic and environmental losses. Satellite imagery is still the main and often the only available source of information for disaster management. In this article, we test the potential of social media to assist disaster management. To this end, we compare insights from two datasets: fire hotspots detected via NASA satellite imagery and almost all GPS-stamped tweets from Sumatra Island, Indonesia, posted during 2014. Sumatra Island is chosen as it regularly experiences a significant number of haze events, which affect citizens in Indonesia as well as in nearby countries including Malaysia and Singapore. We analyse temporal correlations between the datasets and their geo-spatial interdependence. Furthermore, we show how Twitter data reveals changes in users’ behavior during severe haze events. Overall, we demonstrate that social media are a valuable source of complementary and supplementary information for haze disaster management. Based on our methodology and findings, an analytics tool to improve peatland fire and haze disaster management by the Indonesian authorities is under development. Copyright © 2017, The Authors. All rights reserved.

关键词： Wetlands

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Adaptive kNN using expected accuracy for classification of geo-spatial data

arXiv

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arXiv 2017年

作者： Kibanov, Mark Atzmueller, Martin Becker, Martin Hotho, Andreas Mueller, Juergen Stumme, Gerd University of Kassel ITeG Center Knowledge and Data Engineering Wilhelmshöher Allee 73 Kassel34121 Tilburg University WarandelaanTilburg5037 AB Netherlands University of Würzburg DMIR Group Am Hubland Würzburg97074 Germany

The k-Nearest Neighbor (kNN) classification approach is conceptually simple – yet widely applied since it often performs well in practical applications. However, using a global constant k does not always provide an optimal solution, e. g., for datasets with an irregular density distribution of data points. This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized. We define the expected accuracy as the accuracy of a set of structurally similar observations. An arbitrary similarity function can be used to find these observations. We introduce and evaluate different similarity functions. For the evaluation, we use five different classification tasks based on geo-spatial data. Each classification task consists of (tens of) thousands of items. We demonstrate, that the presented expected accuracy measures can be a good estimator for kNN performance, and the proposed adaptive kNN classifier outperforms common kNN and previously introduced adaptive kNN algorithms. Also, we show that the range of considered k can be significantly reduced to speed up the algorithm without negative influence on classification accuracy. Copyright © 2017, The Authors. All rights reserved.

关键词： Classification (of information)

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