PoSTs I & II: Pragmatik und Semantik von kollaborativen Tagging-Systemen

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Ausgewählte Publikationen

Nachfolgend finden Sie eine Liste der wichtigsten Publikationen aus diesem Projekt. Für eine Auflistung aller im Projekt entstandenen Publikationen besuchen Sie bitte die Publikationsliste.

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 = {semantics}, title = {Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning}, year = 2017 }
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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@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 = {sequence}, month = {jul}, title = {MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data}, year = 2017 }
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 (Hrsg.) (2016). 965–974.
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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 = {subtrails}, pages = {965-974}, publisher = {ACM}, title = {Mining Subgroups with Exceptional Transition Behavior.}, year = 2016 }
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 (Hrsg.) (2016). 17–18.
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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 }
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 (Hrsg.) (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 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 = {word2vec}, title = {Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods}, year = 2016 }
Extracting Semantics from Unconstrained Navigation on Wikipedia Niebler, Thomas; Schlör, Daniel; Becker, Martin; Hotho, Andreas in KI (2016). 30(2) 163–168.
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@article{journals/ki/NieblerS0H16, author = {Niebler, Thomas and Schlör, Daniel and Becker, Martin and Hotho, Andreas}, journal = {KI}, keywords = {wikipedia}, number = 2, pages = {163-168}, title = {Extracting Semantics from Unconstrained Navigation on Wikipedia}, volume = 30, year = 2016 }
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 (2016). 10(2) 14:1–14:32.
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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 = {user}, 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 }
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, WWW ’13 Companion, ACM (Hrsg.) (2013). 171–172.
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@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 = {wikipedia}, 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 }

PoSTs I

Kollaborative Verschlagwortungssysteme wie z.B. Delicious und Flickr entwickelten sich in den letzten Jahren als partizipative, skalierbare Alternative zu traditionellen Formen der Informationsorganisation. In solchen System verschlagworten sehr viele Nutzer Ressourcen wie URLs oder Bilder mit Tags. Die so geschaffene Struktur wird Folksonomie genannt, deren emergente Eigenschaften und Strukturen Gegenstand aktueller Forschungen sind und zur Wissensakquisition, dem Lernen von Ontologien oder für das Information Retrieval ausgenutzt werden. Der aktuelle Stand der Forschung bietet allerdings nur unzureichendes Verständnis darüber, wie sich emergente Strukturen in diesen Systemen etablieren und entwickeln bzw. wie sie in Interaktion mit dem Verhalten der Nutzer stehen. Die Idee dieses Projektes ist es, die semantische Analyse von emergenten Strukturen in Folksonomien mit der pragmatischen Analyse von Benutzerverhalten und -motivationen zusammenzuführen und die wechselseitigen Einflüsse systematisch zu untersuchen. Basis der Untersuchungen stellen Vorarbeiten im Bereich der emergenten Bedeutung von Tags und deren Verwendung dar. Das Hauptergebnis dieses Projektes ist ein vertieftes Verständnis der Wechselwirkungen zwischen dem Verhalten der Benutzer, der Entstehung von emergenten semantischen Strukturen und der Möglichkeit, die Eigenschaften von Tagging-Systemen zu beeinflussen.

PoSTs II

Gegenstand des vorliegenden Projektantrags ist die Fortfuhrung der Forschungsarbeiten im derzeit laufenden Projekt “Pragmatik und Semantik in kollaborativen Tagging-Systemen” (PoSTs). Aufbauend auf den bisherigen Erkenntnissen ist es das Ziel des vorliegenden Projektantrags, das Verständnis uber den Zusammenhang zwischen Pragmatik und Semantik in kollaborativen Web-Informationssystemen weiter zu vertiefen und probabilistisch zu modellieren. Der vorliegende Projektantrag erweitert das laufende Projekt in zweierlei Hinsicht: (i) Semantik wurde bisher hauptsächlich durch Heuristiken extrahiert, was in diesem Projekt uber maschinelle Lernverfahren erreicht werden soll. (ii) W ährend Pragmatik bisher zumeist uber die Motivation der Benutzer beleuchtet wurde, so soll im zukunftigen Projekt die Betrachtung auf weitere pragmatische Aspekte, insbesondere die Benutzernavigation unter Verwendung von Markovmodellen, ausgeweitet werden. Auf Basis dieser Arbeiten sollen dann existierende und potentielle Wechselwirkungen untersucht und idealerweise integrative maschinelle Lernverfahren entwickelt werden, die pragmatische und semantische Features besser nutzen und vereinen können.