Publications

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• [ 2016 ]
• [ 2015 ]
• [ 2014 ]
• [ 2013 ]
• [ 2011 ]

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  Extracting and Learning Semantics from Social Web Data. Technical Report (PhD dissertation), . Niebler, Thomas. PhD dissertation. Universit{ä}t W{ü}rzburg, 2019.
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  @phdthesis{niebler2019extracting, author = {Niebler, Thomas}, keywords = {extraction}, school = {Universit{\"a}t W{\"u}rzburg}, title = {Extracting and Learning Semantics from Social Web Data}, type = {doctoralthesis}, year = 2019 }
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  On the Right Track! Analysing and Predicting Navigation Success in Wikipedia. Koopmann, Tobias; Dallmann, Alexander; Hettinger, Lena; Niebler, Thomas; Hotho, Andreas. In Proceedings of the 30th ACM Conference on Hypertext and Social Media, of HT ’19, C. Atzenbeck, J. Rubart, D. E. Millard (eds.), pp. 143–152. ACM, Hof, Germany, 2019.
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  @inproceedings{koopmann2019right, address = {New York, NY, USA}, author = {Koopmann, Tobias and Dallmann, Alexander and Hettinger, Lena and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of the 30th ACM Conference on Hypertext and Social Media}, editor = {Atzenbeck, Claus and Rubart, Jessica and Millard, David E.}, keywords = {behavior}, pages = {143–152}, publisher = {ACM}, series = {HT '19}, title = {On the Right Track! Analysing and Predicting Navigation Success in Wikipedia}, year = 2019 }

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  ClaiRE at SemEval-2018 Task 7-Extended Version. Hettinger, Lena; Dallmann, Alexander; Zehe, Albin; Niebler, Thomas; Hotho, Andreas. In arXiv preprint arXiv:1804.05825. 2018.
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  @article{hettinger2018claire, abstract = {In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, journal = {arXiv preprint arXiv:1804.05825}, keywords = {extraction}, note = {This is the extended version for our work: ClaiRE at SemEval-2018 Task 7}, title = {ClaiRE at SemEval-2018 Task 7-Extended Version}, type = {Publication}, year = 2018 }
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  ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings. Hettinger, Lena; Dallmann, Alexander; Zehe, Albin; Niebler, Thomas; Hotho, Andreas. In Proceedings of International Workshop on Semantic Evaluation (SemEval-2018). New Orleans, LA, USA, 2018.
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  @inproceedings{hettinger2018semeval, address = {New Orleans, LA, USA}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)}, keywords = {classification}, title = {ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings}, year = 2018 }

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  Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas. In Proceedings of the ISWC 2017, N. Nikitina, D. Song, A. Fokoue, P. Haase (eds.). 2017.
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  @inproceedings{niebler2017learning_3, author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas}, booktitle = {Proceedings of the ISWC 2017}, editor = {Nikitina, Nadeschda and Song, Dezhao and Fokoue, Achille and Haase, Peter}, keywords = {solvatio}, title = {Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning}, year = 2017 }
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  Learning Word Embeddings from Tagging Data: A methodological comparison. Niebler, Thomas; Hahn, Luzian; Hotho, Andreas. In Proceedings of the LWDA. 2017.
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  @inproceedings{niebler2017learning_2, author = {Niebler, Thomas and Hahn, Luzian and Hotho, Andreas}, booktitle = {Proceedings of the LWDA}, keywords = {bibsonomy}, title = {Learning Word Embeddings from Tagging Data: A methodological comparison}, year = 2017 }
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  Learning Semantic Relatedness From Human Feedback Using Metric Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas. In arXiv preprint arXiv:1705.07425. 2017.
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  @article{niebler2017learning, abstract = {Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. For this, we argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost. We rigorously test our approach on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We find that our method can significantly improve semantic relatedness measures by learning from additional information, such as explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for ontology and recommendation engineers, but also for any other researchers and practitioners of Semantic Web techniques.}, author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas}, journal = {arXiv preprint arXiv:1705.07425}, keywords = {selected}, note = {cite arxiv: 1705.07425}, title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning}, type = {Publication}, year = 2017 }

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  FolkTrails: Interpreting Navigation Behavior in a Social Tagging System. Niebler, Thomas; Becker, Martin; Zoller, Daniel; Doerfel, Stephan; Hotho, Andreas. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, of CIKM ’16. ACM, 2016.
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  @inproceedings{niebler2016folktrails, abstract = {Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.}, author = {Niebler, Thomas and Becker, Martin and Zoller, Daniel and Doerfel, Stephan and Hotho, Andreas}, booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management}, keywords = {selected}, publisher = {ACM}, series = {CIKM '16}, title = {FolkTrails: Interpreting Navigation Behavior in a Social Tagging System}, year = 2016 }
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  Extracting Semantics from Random Walks on Wikipedia: Comparing Learning and Counting Methods. Dallmann, Alexander; Niebler, Thomas; Lemmerich, Florian; Hotho, Andreas. In Proceedings of the 10th International Conference on Web and Social Media, R. West, L. Zia, D. Taraborelli, J. Leskovec (eds.). AAAI, 2016.
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  @inproceedings{dallmann2016extracting, abstract = {Semantic relatedness between words has been extracted from a variety of sources.In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representation learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.}, author = {Dallmann, Alexander and Niebler, Thomas and Lemmerich, Florian and Hotho, Andreas}, booktitle = {Proceedings of the 10th International Conference on Web and Social Media}, editor = {West, Robert and Zia, Leila and Taraborelli, Dario and Leskovec, Jure}, keywords = {solvatio}, publisher = {AAAI}, title = {Extracting Semantics from Random Walks on Wikipedia: Comparing Learning and Counting Methods}, year = 2016 }
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  Extracting Semantics from Unconstrained Navigation on Wikipedia. Niebler, Thomas; Schlör, Daniel; Becker, Martin; Hotho, Andreas. In KI -- Künstliche Intelligenz, 30(2), pp. 163–168. Springer, 2016.
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  @article{niebler2016extracting, abstract = {Semantic relatedness between words has been successfully extracted from navigation on Wikipedia pages. However, the navigational data used in the corresponding works are sparse and expected to be biased since they have been collected in the context of games. In this paper, we raise this limitation and explore if semantic relatedness can also be extracted from unconstrained navigation. To this end, we first highlight structural differences between unconstrained navigation and game data. Then, we adapt a state of the art approach to extract semantic relatedness on Wikipedia paths. We apply this approach to transitions derived from two unconstrained navigation datasets as well as transitions from WikiGame and compare the results based on two common gold standards. We confirm expected structural differences when comparing unconstrained navigation with the paths collected by WikiGame. In line with this result, the mentioned state of the art approach for semantic extraction on navigation data does not yield good results for unconstrained navigation. Yet, we are able to derive a relatedness measure that performs well on both unconstrained navigation data as well as game data. Overall, we show that unconstrained navigation data on Wikipedia is suited for extracting semantics.}, author = {Niebler, Thomas and Schlör, Daniel and Becker, Martin and Hotho, Andreas}, journal = {KI – Künstliche Intelligenz}, keywords = {selected}, number = 2, pages = {163–168}, publisher = {Springer}, title = {Extracting Semantics from Unconstrained Navigation on Wikipedia}, volume = 30, year = 2016 }
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  What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy. Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus. In ACM Transactions on the Web, 10(2), pp. 14:1–14:32. 2016.
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  @article{doerfel2016users, abstract = {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 aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, journal = {ACM Transactions on the Web}, keywords = {equality}, number = 2, pages = {14:1–14:32}, title = {What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy}, volume = 10, year = 2016 }

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  Evaluating Emergent Semantics in Folksonomies on Human Intuition. Niebler, Thomas; Becker, Martin; Zoller, Daniel; Doerfel, Stephan; Hotho, Andreas. 2015.
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  @techreport{tr_niebler2015evaluating, author = {Niebler, Thomas and Becker, Martin and Zoller, Daniel and Doerfel, Stephan and Hotho, Andreas}, keywords = {intuition}, title = {Evaluating Emergent Semantics in Folksonomies on Human Intuition}, year = 2015 }
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  Extracting Semantic Relatedness from Navigation in a Social Tagging System. Niebler, Thomas; Becker, Martin; Zoller, Daniel; Doerfel, Stephan; Hotho, Andreas. 2015.
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  @techreport{tr_niebler2015extracting, author = {Niebler, Thomas and Becker, Martin and Zoller, Daniel and Doerfel, Stephan and Hotho, Andreas}, keywords = {bibsonomy}, title = {Extracting Semantic Relatedness from Navigation in a Social Tagging System}, year = 2015 }
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  ConDist: A Context-Driven Categorical Distance Measure. Ring, Markus; Otto, Florian; Becker, Martin; Niebler, Thomas; Landes, Dieter; Hotho, Andreas. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Vol. 9284 of Lecture Notes in Computer Science, A. Appice, P. P. Rodrigues, V. S. Costa, C. Soares, J. Gama, A. Jorge (eds.), pp. 251–266. Springer International Publishing, 2015.
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  @inproceedings{ring2015condist, abstract = {A distance measure between objects is a key requirement for many data mining tasks like clustering, classification or outlier detection. However, for objects characterized by categorical attributes, defining meaningful distance measures is a challenging task since the values within such attributes have no inherent order, especially without additional domain knowledge. In this paper, we propose an unsupervised distance measure for objects with categorical attributes based on the idea that categorical attribute values are similar if they appear with similar value distributions on correlated context attributes. Thus, the distance measure is automatically derived from the given data set. We compare our new distance measure to existing categorical distance measures and evaluate on different data sets from the UCI machine-learning repository. The experiments show that our distance measure is recommendable, since it achieves similar or better results in a more robust way than previous approaches.}, author = {Ring, Markus and Otto, Florian and Becker, Martin and Niebler, Thomas and Landes, Dieter and Hotho, Andreas}, booktitle = {Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases}, editor = {Appice, Annalisa and Rodrigues, Pedro Pereira and Costa, Vítor Santos and Soares, Carlos and Gama, João and Jorge, Alípio}, keywords = {categorical}, pages = {251-266}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, title = {ConDist: A Context-Driven Categorical Distance Measure}, volume = 9284, year = 2015 }

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  Folksonomies. Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus. In Encyclopedia of Social Network Analysis and Mining, pp. 542–547. Springer, 2014.
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  @incollection{singer2014folksonomies, author = {Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, keywords = {posts}, pages = {542–547}, publisher = {Springer}, title = {Folksonomies}, year = 2014 }
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  Of course we share! Testing Assumptions about Social Tagging Systems. Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus. 2014.
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  @techreport{doerfel2014course, abstract = {Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world 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 and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior.}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, keywords = {share}, note = {cite arxiv:1401.0629}, title = {Of course we share! Testing Assumptions about Social Tagging Systems}, year = 2014 }
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  How Social is Social Tagging?. Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas. In Proceedings of the 23rd International World Wide Web Conference, of WWW 2014, pp. 251–252. ACM, 2014.
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  @inproceedings{doerfel2014social, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Strohmaier, Markus and Hotho, Andreas}, booktitle = {Proceedings of the 23rd International World Wide Web Conference}, keywords = {bibsonomy}, pages = {251–252}, publisher = {ACM}, series = {WWW 2014}, title = {How Social is Social Tagging?}, year = 2014 }

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  How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems. Niebler, Thomas; Singer, Philipp; Benz, Dominik; Körner, Christian; Strohmaier, Markus; Hotho, Andreas. In ECIR, pp. 86–97. Springer, 2013.
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  @inproceedings{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 = {ECIR}, keywords = {influence}, pages = {86-97}, publisher = {Springer}, title = {How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems}, year = 2013 }
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  Computing Semantic Relatedness from Human Navigational Paths on Wikipedia. Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas. In Proceedings of the 22nd International Conference on World Wide Web, L. Carr, A. H. F. Laender, B. F. Lóscio, I. King, M. Fontoura, D. Vrandecic, L. Aroyo, J. P. M. de Oliveira, F. Lima, E. Wilde (eds.), pp. 171–172. ACM, 2013.
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  @inproceedings{singer2013computing_www, abstract = {This paper presents a novel approach for computing semantic relatedness between concepts on Wikipedia by using human navigational paths for this task. Our results suggest that human navigational paths provide a viable source for calculating semantic relatedness between concepts on Wikipedia. We also show that we can improve accuracy by intelligent selection of path corpora based on path characteristics indicating that not all paths are equally useful. Our 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}, booktitle = {Proceedings of the 22nd International Conference on World Wide Web}, editor = {Carr, Leslie and Laender, Alberto H. F. and Lóscio, Bernadette Farias and King, Irwin and Fontoura, Marcus and Vrandecic, Denny and Aroyo, Lora and de Oliveira, José Palazzo M. and Lima, Fernanda and Wilde, Erik}, keywords = {extraction}, organization = {ACM}, pages = {171–172}, publisher = {ACM}, title = {Computing Semantic Relatedness from Human Navigational Paths on Wikipedia}, year = 2013 }
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  Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia. Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas. In IJSWIS, 9(4), pp. 41–70. IGI Global, 2013.
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  @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}, journal = {IJSWIS}, keywords = {journal15}, number = 4, pages = {41–70}, publisher = {IGI Global}, title = {Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia}, volume = 9, year = 2013 }

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  Clusteralgorithmen für Multisatellitensysteme. Technical Report (Master thesis), . Niebler, Thomas. Master thesis. University of Wuerzburg, 2011, September.
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  @mastersthesis{niebler2011clusteralgorithmen, author = {Niebler, Thomas}, keywords = {satellite}, month = {09}, school = {University of Wuerzburg}, title = {Clusteralgorithmen für Multisatellitensysteme}, type = {Publication}, year = 2011 }