@article{kluegl2013exploiting,
  abstract = {Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.},
  author = {Kluegl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank},
  interhash = {9ef3f543e4cc9e2b0ef078595f92013b},
  intrahash = {fbaab25e96dd20d96ece9d7fefdc3b4f},
  journal = {Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics},
  pages = {111-125},
  title = {Exploiting Structural Consistencies with Stacked Conditional Random Fields},
  volume = 30,
  year = 2013
}

@article{singer2013computing,
  abstract = {In this article, the authors present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia’s content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment – a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors’ results are intriguing: They suggest that (i) semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia’s plain link structure alone and (ii) that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors’ work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.},
  author = {Singer, Philipp and Niebler, Thomas and Strohmaier, Markus and Hotho, Andreas},
  doi = {10.4018/ijswis.2013100103},
  interhash = {3377abe1838bd1f650b317ed1fca4dfe},
  intrahash = {5262c48a2e2791d28610712e3bf5cf55},
  issn = {15526283},
  journal = {International Journal on Semantic Web and Information Systems (IJSWIS)},
  number = 4,
  pages = {41--70},
  publisher = {IGI Global},
  refid = {102707},
  title = {Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia},
  url = {http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijswis.2013100103},
  volume = 9,
  year = 2013
}

@proceedings{conf/recsys/2013rsweb,
  booktitle = {RSWeb@RecSys},
  editor = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Hotho, Andreas and Anand, Sarabjot Singh and Guy, Ido},
  ee = {http://ceur-ws.org/Vol-1066},
  interhash = {31e724c09d1f4a4bbf013ecb8e1f6685},
  intrahash = {aca768068f09003e97b51d48ec092ddc},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.},
  url = {http://ceur-ws.org/Vol-1066},
  volume = 1066,
  year = 2013
}

@book{atzmueller2013ubiquitous,
  address = {Berlin, Heidelberg},
  editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas},
  interhash = {b0fcec93b875c8b0060087bc07944e89},
  intrahash = {1e2d036351662d35ef95719554d37e46},
  isbn = {9783642453915 3642453910 9783642453922 3642453929},
  publisher = {Imprint: Springer},
  refid = {867052137},
  title = {Ubiquitous Social Media Analysis Third International Workshops, MUSE 2012, Bristol, UK, September 24, 2012, and MSM 2012, Milwaukee, WI, USA, June 25, 2012, Revised Selected Papers},
  url = {http://link.springer.com/book/10.1007/978-3-642-45392-2},
  year = 2013
}

@inproceedings{MASH:13b,
  address = {Bamberg, Germany},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proc. LWA 2013 (KDML Special Track)},
  interhash = {73088600a500f7d06768615d6e1c2b3d},
  intrahash = {820ffb2166b330bf60bb30b16e426553},
  publisher = {University of Bamberg},
  title = {{On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)}},
  year = 2011
}

@book{doerfel2013informationelle,
  author = {Doerfel, Stephan and Hotho, Andreas and Kartal-Aydemir, Aliye and Roßnagel, Alexander and Stumme, Gerd},
  interhash = {f72d297ba42797ca66baba052c846b7a},
  intrahash = {2bb934c0ff3652843fd0aff97d8d7324},
  isbn = {9783642380556 3642380557},
  publisher = {Vieweg + Teubner Verlag},
  refid = {857973438},
  title = {Informationelle Selbstbestimmung Im Web 2.0 Chancen Und Risiken Sozialer Verschlagwortungssysteme},
  url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9783642380556},
  year = 2013
}

@inproceedings{mueller2013recommendations,
  abstract = {With the rising popularity of smart mobile devices, sensor data-based
 applications have become more and more popular. Their users record
 data during their daily routine or specifically for certain events.
 The application WideNoise Plus allows users to record sound samples
 and to annotate them with perceptions and tags. The app is being
 used to document and map the soundscape all over the world. The procedure
 of recording, including the assignment of tags, has to be as easy-to-use
 as possible. We therefore discuss the application of tag recommender
 algorithms in this particular scenario. We show, that this task is
 fundamentally different from the well-known tag recommendation problem
 in folksonomies as users do no longer tag fix resources but rather
 sensory data and impressions. The scenario requires efficient recommender
 algorithms that are able to run on the mobile device, since Internet
 connectivity cannot be assumed to be available. Therefore, we evaluate
 the performance of several tag recommendation algorithms and discuss
 their applicability in the mobile sensing use-case.},
  address = {Aachen, Germany},
  author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings},
  interhash = {23d1cf49208d9a0c8b883dc69d4e444d},
  intrahash = {2bab3f013052bc741e795c5c61aea5c9},
  issn = {1613-0073},
  publisher = {CEUR-WS},
  title = {Tag Recommendations for SensorFolkSonomies},
  url = {http://ceur-ws.org/Vol-1066/},
  volume = 1066,
  year = 2013
}

@article{10.1371/journal.pone.0081638,
  abstract = {<p>The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.</p>},
  author = {Becker, Martin and Caminiti, Saverio and Fiorella, Donato and Francis, Louise and Gravino, Pietro and Haklay, Mordechai (Muki) and Hotho, Andreas and Loreto, Vittorio and Mueller, Juergen and Ricchiuti, Ferdinando and Servedio, Vito D. P. and Sîrbu, Alina and Tria, Francesca},
  doi = {10.1371/journal.pone.0081638},
  interhash = {52652b4fe271d8be4b96b2f692fe9519},
  intrahash = {423a8aaa4eb317ee507143293205c76f},
  journal = {PLoS ONE},
  month = {12},
  number = 12,
  pages = {e81638},
  publisher = {Public Library of Science},
  title = {Awareness and Learning in Participatory Noise Sensing},
  url = {http://dx.doi.org/10.1371%2Fjournal.pone.0081638},
  volume = 8,
  year = 2013
}

@article{landia2013deeper,
  abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@incollection{MASH:13,
  address = {Heidelberg, Germany},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Complex Networks IV},
  doi = {10.1007/978-3-642-36844-8_2},
  editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert},
  interhash = {bf333426bb7af5f01bf0c465c1bfe1fc},
  intrahash = {0a35f1ed66fcd342a6a44d70c63fb735},
  optisbn = {978-3-642-36843-1},
  opturl = {http://dx.doi.org/10.1007/978-3-642-36844-8_2},
  publisher = {Springer Verlag},
  series = {Studies in Computational Intelligence},
  title = {{Semantics of User Interaction in Social Media}},
  volume = 476,
  year = 2013
}

@inproceedings{mueller-2013a,
  abstract = {An increasing number of platforms like Xively or ThingSpeak are available to manage ubiquitous sensor data enabling the Internet of Things. Strict data formats allow interoperability and informative visualizations, supporting the development of custom user applications. Yet, these strict data formats as well as the common feed-centric approach limit the flexibility of these platforms. We aim at providing a concept that supports data ranging from text-based formats like JSON to images and video footage. Furthermore, we introduce the concept of extensions, which allows to enrich existing data points with additional information, thus, taking a data point centric approach. This enables us to gain semantic and user specific context by attaching subjective data to objective values. This paper provides an overview of our architecture including concept, implementation details and present applications. We distinguish our approach from several other systems and describe two sensing applications namely AirProbe and WideNoise that were implemented for our platform.},
  author = {Becker, Martin and Mueller, Juergen and Hotho, Andreas and Stumme, Gerd},
  booktitle = {1st International Workshop on Pervasive Urban Crowdsensing Architecture and Applications, PUCAA 2013, Zurich, Switzerland -- September 9, 2013. Proceedings},
  interhash = {5302866e7849d40a44deab166b6c4d36},
  intrahash = {35eef1ecdac9d83d3bfbcac22c31984a},
  note = {Accepted for publication},
  pages = {New York, NY, USA},
  publisher = {ACM},
  title = {A Generic Platform for Ubiquitous and Subjective Data},
  year = 2013
}

@incollection{niebler2013tagging,
  abstract = {The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which },
  author = {Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas},
  booktitle = {Advances in Information Retrieval},
  doi = {10.1007/978-3-642-36973-5_8},
  editor = {Serdyukov, Pavel and Braslavski, Pavel and Kuznetsov, SergeiO. and Kamps, Jaap and Rüger, Stefan and Agichtein, Eugene and Segalovich, Ilya and Yilmaz, Emine},
  interhash = {8f11f2140d9eb369a7ca42cd527f76c1},
  intrahash = {8583743a7598e78cc7b4e8af71a43902},
  isbn = {978-3-642-36972-8},
  pages = {86-97},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-36973-5_8},
  volume = 7814,
  year = 2013
}