Publications

• [ 2017 ]
• [ 2016 ]
• [ 2015 ]
• [ 2014 ]

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  MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data. Becker, Martin; Lemmerich, Florian; Singer, Philipp; Strohmaier, Markus; Hotho, Andreas. In Data Mining and Knowledge Discovery. 2017.
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  Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.

  @article{becker2017mixedtrails, abstract = {Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.}, author = {Becker, Martin and Lemmerich, Florian and Singer, Philipp and Strohmaier, Markus and Hotho, Andreas}, journal = {Data Mining and Knowledge Discovery}, keywords = {selected}, month = {07}, title = {MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data}, year = 2017 }
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  Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas. In ISWC’17. 2017.
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  @inproceedings{niebler2017learning_3, author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas}, booktitle = {ISWC'17}, keywords = {selected}, title = {Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning}, year = 2017 }
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  Learning Semantic Relatedness From Human Feedback Using Metric Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas. 2017.
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  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.

  @misc{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}, keywords = {postsii}, note = {cite arxiv:1705.07425}, title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning}, year = 2017 }

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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 Wiki Workshop@ICWSM, R. West, L. Zia, D. Taraborelli, J. Leskovec (eds.). 2016.
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  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 representa- tion 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.

  @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 representa- tion 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 = {Wiki Workshop@ICWSM}, editor = {West, Robert and Zia, Leila and Taraborelli, Dario and Leskovec, Jure}, keywords = {selected}, title = {Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods}, year = 2016 }
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  Mining Subgroups with Exceptional Transition Behavior. Lemmerich, Florian; Becker, Martin; Singer, Philipp; Helic, Denis; Hotho, Andreas; Strohmaier, Markus. In KDD, B. Krishnapuram, M. Shah, A. J. Smola, C. Aggarwal, D. Shen, R. Rastogi (eds.), pp. 965–974. ACM, 2016.
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  @inproceedings{lemmerich2016mining, author = {Lemmerich, Florian and Becker, Martin and Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, booktitle = {KDD}, crossref = {conf/kdd/2016}, editor = {Krishnapuram, Balaji and Shah, Mohak and Smola, Alexander J. and Aggarwal, Charu and Shen, Dou and Rastogi, Rajeev}, keywords = {selected}, pages = {965-974}, publisher = {ACM}, title = {Mining Subgroups with Exceptional Transition Behavior.}, year = 2016 }
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  SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails. Becker, Martin; Mewes, Hauke; Hotho, Andreas; Dimitrov, Dimitar; Lemmerich, Florian; Strohmaier, Markus. In WWW (Companion Volume), J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks, B. Y. Zhao (eds.), pp. 17–18. ACM, 2016.
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  @inproceedings{becker2016sparktrails, author = {Becker, Martin and Mewes, Hauke and Hotho, Andreas and Dimitrov, Dimitar and Lemmerich, Florian and Strohmaier, Markus}, booktitle = {WWW (Companion Volume)}, crossref = {conf/www/2016c}, editor = {Bourdeau, Jacqueline and Hendler, Jim and Nkambou, Roger and Horrocks, Ian and Zhao, Ben Y.}, keywords = {selected}, pages = {17-18}, publisher = {ACM}, title = {SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails.}, 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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  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.

  @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 = {selected}, 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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  Extracting Semantics from Unconstrained Navigation on Wikipedia. Niebler, Thomas; Schlör, Daniel; Becker, Martin; Hotho, Andreas. In KI, 30(2), pp. 163–168. 2016.
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  @article{journals/ki/NieblerS0H16, author = {Niebler, Thomas and Schlör, Daniel and Becker, Martin and Hotho, Andreas}, journal = {KI}, keywords = {selected}, number = 2, pages = {163-168}, title = {Extracting Semantics from Unconstrained Navigation on Wikipedia}, volume = 30, year = 2016 }
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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, New York, NY, USA, 2016.
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  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.

  @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.}, address = {New York, NY, USA}, 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 = {behavior}, publisher = {ACM}, series = {CIKM '16}, title = {FolkTrails: Interpreting Navigation Behavior in a Social Tagging System}, year = 2016 }

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  Photowalking the city: comparing hypotheses about urban photo trails on Flickr. Becker, Martin; Singer, Philipp; Lemmerich, Florian; Hotho, Andreas; Helic, Denis; Strohmaier, Markus. In Proceedings of the 7th International Conference on SocialInformatics (to appear). Beijing, China, 2015.
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  @inproceedings{becker2015photowalking, address = {Beijing, China}, author = {Becker, Martin and Singer, Philipp and Lemmerich, Florian and Hotho, Andreas and Helic, Denis and Strohmaier, Markus}, booktitle = {Proceedings of the 7th International Conference on SocialInformatics (to appear)}, keywords = {behavior}, month = 12, title = {Photowalking the city: comparing hypotheses about urban photo trails on Flickr}, type = {Publication}, year = 2015 }
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  MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform. Becker, Martin; Borchert, Kathrin; Hirth, Matthias; Mewes, Hauke; Hotho, Andreas; Tran-Gia, Phuoc. In Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, of i-KNOW ’15, pp. 10:1–10:8. ACM, New York, NY, USA, 2015.
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  @inproceedings{becker2015microtrails, address = {New York, NY, USA}, author = {Becker, Martin and Borchert, Kathrin and Hirth, Matthias and Mewes, Hauke and Hotho, Andreas and Tran-Gia, Phuoc}, booktitle = {Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business}, keywords = {incentives}, month = 10, pages = {10:1–10:8}, publisher = {ACM}, series = {i-KNOW '15}, title = {MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform}, year = 2015 }
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  The Role of Structural Information for Designing Navigational User Interfaces. Dimitrov, Dimitar; Singer, Philipp; Helic, Denis; Strohmaier, Markus. In Proceedings of the 26th ACM Conference on Hypertext \&\#38; Social Media, of HT ’15, pp. 59–68. ACM, Guzelyurt, Northern Cyprus, 2015.
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  Today, a variety of user interfaces exists for navigating information spaces, including, for example, tag clouds, breadcrumbs, subcategories and others. However, such navigational user interfaces are only useful to the extent that they expose the underlying topology---or network structure---of the information space. Yet, little is known about which topological clues should be integrated in navigational user interfaces. In detail, the aim of this paper is to identify what kind of and how much topological information needs to be included in user interfaces to facilitate efficient navigation. We model navigation as a variation of a decentralized search process with partial information and study its sensitivity to the quality and amount of the structural information used for navigation. We experiment with two strategies for node selection (quality of structural information provided to the user) and different amount of information (amount of structural information provided to the user). Our experiments on four datasets from different domains show that efficient navigation depends on the kind of structural information utilized. Additionally, node properties differ in their quality for augmenting navigation and intelligent pre-selection of which nodes to present in the interface to the user can improve navigational efficiency. This suggests that only a limited amount of high quality structural information needs to be exposed through the navigational user interface.

  @inproceedings{dimitrov2015structural, abstract = {Today, a variety of user interfaces exists for navigating information spaces, including, for example, tag clouds, breadcrumbs, subcategories and others. However, such navigational user interfaces are only useful to the extent that they expose the underlying topology—or network structure—of the information space. Yet, little is known about which topological clues should be integrated in navigational user interfaces. In detail, the aim of this paper is to identify what kind of and how much topological information needs to be included in user interfaces to facilitate efficient navigation. We model navigation as a variation of a decentralized search process with partial information and study its sensitivity to the quality and amount of the structural information used for navigation. We experiment with two strategies for node selection (quality of structural information provided to the user) and different amount of information (amount of structural information provided to the user). Our experiments on four datasets from different domains show that efficient navigation depends on the kind of structural information utilized. Additionally, node properties differ in their quality for augmenting navigation and intelligent pre-selection of which nodes to present in the interface to the user can improve navigational efficiency. This suggests that only a limited amount of high quality structural information needs to be exposed through the navigational user interface.}, address = {New York, NY, USA}, author = {Dimitrov, Dimitar and Singer, Philipp and Helic, Denis and Strohmaier, Markus}, booktitle = {Proceedings of the 26th ACM Conference on Hypertext \&\#38; Social Media}, keywords = {interface}, pages = {59–68}, publisher = {ACM}, series = {HT '15}, title = {The Role of Structural Information for Designing Navigational User Interfaces}, year = 2015 }
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  VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories. Becker, Martin; Singer, Philipp; Lemmerich, Florian; Hotho, Andreas; Helic, Denis; Strohmaier, Markus. In Proceedings of the 26th ACM Conference on Hypertext \&\#38; Social Media, of HT ’15, pp. 319–320. ACM, Guzelyurt, Northern Cyprus, 2015.
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  Understanding the way people move through urban areas represents an important problem that has implications for a range of societal challenges such as city planning, public transportation, or crime analysis. In this paper, we present an interactive visualization tool called VizTrails for exploring and understanding such human movement. It features visualizations that show aggregated statistics of trails for geographic areas that correspond to grid cells on a map, e.g., on the number of users passing through or on cells commonly visited next. Amongst other features, system allows to overlay the map with the results of SPARQL queries in order to relate the observed trajectory statistics with its geo-spatial context, e.g., considering a city's points of interest. The systems functionality is demonstrated using trajectory examples extracted from the social photo sharing platform Flickr. Overall, VizTrails facilitates deeper insights into geo-spatial trajectory data by enabling interactive exploration of aggregated statistics and providing geo-spatial context.

  @inproceedings{Becker:2015:VIV:2700171.2791021, abstract = {Understanding the way people move through urban areas represents an important problem that has implications for a range of societal challenges such as city planning, public transportation, or crime analysis. In this paper, we present an interactive visualization tool called VizTrails for exploring and understanding such human movement. It features visualizations that show aggregated statistics of trails for geographic areas that correspond to grid cells on a map, e.g., on the number of users passing through or on cells commonly visited next. Amongst other features, system allows to overlay the map with the results of SPARQL queries in order to relate the observed trajectory statistics with its geo-spatial context, e.g., considering a city's points of interest. The systems functionality is demonstrated using trajectory examples extracted from the social photo sharing platform Flickr. Overall, VizTrails facilitates deeper insights into geo-spatial trajectory data by enabling interactive exploration of aggregated statistics and providing geo-spatial context.}, address = {New York, NY, USA}, author = {Becker, Martin and Singer, Philipp and Lemmerich, Florian and Hotho, Andreas and Helic, Denis and Strohmaier, Markus}, booktitle = {Proceedings of the 26th ACM Conference on Hypertext \&\#38; Social Media}, keywords = {from:hotho}, pages = {319–320}, publisher = {ACM}, series = {HT '15}, title = {VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories}, year = 2015 }

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  How Social is Social Tagging?. Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus. In Proceedings of the 23rd International World Wide Web Conference, of WWW 2014. ACM, New York, NY, USA, 2014.
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  @inproceedings{doerfel2014social, address = {New York, NY, USA}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Proceedings of the 23rd International World Wide Web Conference}, keywords = {behavior}, publisher = {ACM}, series = {WWW 2014}, title = {How Social is Social Tagging?}, 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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  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.

  @misc{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 = {from:hotho}, note = {cite arxiv:1401.0629}, title = {Of course we share! Testing Assumptions about Social Tagging Systems}, year = 2014 }

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  Semantic stability in social tagging streams. Wagner, Claudia; Singer, Philipp; Strohmaier, Markus; Huberman, Bernardo A. In Proceedings of the 23rd international conference on World wide web, pp. 735–746. ACM, 2014.
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  @inproceedings{wagner2014semantic, author = {Wagner, Claudia and Singer, Philipp and Strohmaier, Markus and Huberman, Bernardo A}, booktitle = {Proceedings of the 23rd international conference on World wide web}, keywords = {stability}, organization = {ACM}, pages = {735–746}, title = {Semantic stability in social tagging streams}, year = 2014 }
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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 = {from:hotho}, pages = {542–547}, publisher = {Springer}, title = {Folksonomies}, year = 2014 }
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  Evolution of Reddit: From the Front Page of the Internet to a Self-referential Community?. Singer, Philipp; Fl{ö}ck, Fabian; Meinhart, Clemens; Zeitfogel, Elias; Strohmaier, Markus. In Proceedings of the 23rd International Conference on World Wide Web, of WWW ’14 Companion, pp. 517–522. ACM, Seoul, Korea, 2014.
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  In the past few years, Reddit -- a community-driven platform for submitting, commenting and rating links and text posts -- has grown exponentially, from a small community of users into one of the largest online communities on the Web. To the best of our knowledge, this work represents the most comprehensive longitudinal study of Reddit's evolution to date, studying both (i) how user submissions have evolved over time and (ii) how the community's allocation of attention and its perception of submissions have changed over 5 years based on an analysis of almost 60 million submissions. Our work reveals an ever-increasing diversification of topics accompanied by a simultaneous concentration towards a few selected domains both in terms of posted submissions as well as perception and attention. By and large, our investigations suggest that Reddit has transformed itself from a dedicated gateway to the Web to an increasingly self-referential community that focuses on and reinforces its own user-generated image- and textual content over external sources.

  @inproceedings{Singer:2014:ERF:2567948.2576943, abstract = {In the past few years, Reddit – a community-driven platform for submitting, commenting and rating links and text posts – has grown exponentially, from a small community of users into one of the largest online communities on the Web. To the best of our knowledge, this work represents the most comprehensive longitudinal study of Reddit's evolution to date, studying both (i) how user submissions have evolved over time and (ii) how the community's allocation of attention and its perception of submissions have changed over 5 years based on an analysis of almost 60 million submissions. Our work reveals an ever-increasing diversification of topics accompanied by a simultaneous concentration towards a few selected domains both in terms of posted submissions as well as perception and attention. By and large, our investigations suggest that Reddit has transformed itself from a dedicated gateway to the Web to an increasingly self-referential community that focuses on and reinforces its own user-generated image- and textual content over external sources.}, address = {New York, NY, USA}, author = {Singer, Philipp and Fl\"{o}ck, Fabian and Meinhart, Clemens and Zeitfogel, Elias and Strohmaier, Markus}, booktitle = {Proceedings of the 23rd International Conference on World Wide Web}, keywords = {user}, pages = {517–522}, publisher = {ACM}, series = {WWW '14 Companion}, title = {Evolution of Reddit: From the Front Page of the Internet to a Self-referential Community?}, year = 2014 }
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  Understanding, Leveraging and Improving Human Navigation on the Web. Singer, Philipp. In Proceedings of the 23rd International Conference on World Wide Web, of WWW ’14 Companion, pp. 27–32. ACM, Seoul, Korea, 2014.
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  Navigating websites represents a fundamental activity of users on the Web. Modeling this activity, i.e., understanding how predictable human navigation is and whether regularities can be detected has been of interest to researchers for nearly two decades. This is crucial for improving the Web experience of users by e.g., enhancing interfaces or information network structures. This thesis envisions to shedding light on human navigational patterns by trying to understand, leverage and improve human navigation on the Web. One main goal of this thesis is the construction of a versatile framework for modeling human navigational data with the use of Markov chains and for detecting the appropriate Markov chain order by using several advanced inference methods. It allows us to investigate memory and structure in human navigation patterns. Furthermore, we are interested in detecting whether pragmatic human navigational data can be leveraged by e.g., being useful for the task of calculating semantic relatedness between concepts. Finally, we want to find ways of enhancing human navigation models. Concretely, we plan on incorporating prior knowledge about the semantic relatedness between concepts to our Markov chain models as it is known that humans navigate the Web intuitively instead of randomly. Our experiments should be conducted on a variety of distinct navigational data including both goal oriented and free form navigation scenarios. We not only look at navigational paths over websites, but also abstract away to navigational paths over topics in order to get insights into cognitive patterns.

  @inproceedings{Singer:2014:ULI:2567948.2567956, abstract = {Navigating websites represents a fundamental activity of users on the Web. Modeling this activity, i.e., understanding how predictable human navigation is and whether regularities can be detected has been of interest to researchers for nearly two decades. This is crucial for improving the Web experience of users by e.g., enhancing interfaces or information network structures. This thesis envisions to shedding light on human navigational patterns by trying to understand, leverage and improve human navigation on the Web. One main goal of this thesis is the construction of a versatile framework for modeling human navigational data with the use of Markov chains and for detecting the appropriate Markov chain order by using several advanced inference methods. It allows us to investigate memory and structure in human navigation patterns. Furthermore, we are interested in detecting whether pragmatic human navigational data can be leveraged by e.g., being useful for the task of calculating semantic relatedness between concepts. Finally, we want to find ways of enhancing human navigation models. Concretely, we plan on incorporating prior knowledge about the semantic relatedness between concepts to our Markov chain models as it is known that humans navigate the Web intuitively instead of randomly. Our experiments should be conducted on a variety of distinct navigational data including both goal oriented and free form navigation scenarios. We not only look at navigational paths over websites, but also abstract away to navigational paths over topics in order to get insights into cognitive patterns.}, address = {New York, NY, USA}, author = {Singer, Philipp}, booktitle = {Proceedings of the 23rd International Conference on World Wide Web}, keywords = {posts}, pages = {27–32}, publisher = {ACM}, series = {WWW '14 Companion}, title = {Understanding, Leveraging and Improving Human Navigation on the Web}, 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 Advances in Information Retrieval, Vol. 7814, P. Serdyukov, P. Braslavski, S. Kuznetsov, J. Kamps, S. Rüger, E. Agichtein, I. Segalovich, E. Yilmaz (eds.), pp. 86–97. Springer Berlin Heidelberg, 2013.
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  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

  @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}, editor = {Serdyukov, Pavel and Braslavski, Pavel and Kuznetsov, SergeiO. and Kamps, Jaap and Rüger, Stefan and Agichtein, Eugene and Segalovich, Ilya and Yilmaz, Emine}, keywords = {from:hotho}, 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}, volume = 7814, 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 companion, of WWW ’13 Companion, ACM (ed.), pp. 171–172. International World Wide Web Conferences Steering Committee, Rio de Janeiro, Brazil, 2013.
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  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.

  @inproceedings{singer2013computing, 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.}, address = {Republic and Canton of Geneva, Switzerland}, author = {Singer, Philipp and Niebler, Thomas and Strohmaier, Markus and Hotho, Andreas}, booktitle = {Proceedings of the 22nd international conference on World Wide Web companion}, editor = {ACM}, keywords = {selected}, pages = {171–172}, publisher = {International World Wide Web Conferences Steering Committee}, series = {WWW '13 Companion}, 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 International Journal on Semantic Web and Information Systems (IJSWIS), 9(4), pp. 41–70. IGI Global, 2013.
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  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.

  @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 = {International Journal on Semantic Web and Information Systems (IJSWIS)}, keywords = {from:hotho}, 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 }