Mining social semantics on the social web.Hotho, A.; Jaeschke, R.; Lerman, K. in Semantic Web (2017). 8(5) 623--624.
Learning Word Embeddings from Tagging Data: A methodological comparison.Niebler, Thomas; Hahn, Luzian; Hotho, Andreas (2017).
Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning.Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas (2017).
Learning Semantic Relatedness From Human Feedback Using Metric Learning.Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas (2017).
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.
FolkTrails: Interpreting Navigation Behavior in a Social Tagging System.Niebler, Thomas; Becker, Martin; Zoller, Daniel; Doerfel, Stephan; Hotho, Andreas in CIKM '16 (2016).
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.
Extracting Semantics from Unconstrained Navigation on Wikipedia.Niebler, Thomas; Schlör, Daniel; Becker, Martin; Hotho, Andreas in KI (2016). 30(2) 163-168.
Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods.Dallmann, Alexander; Niebler, Thomas; Lemmerich, Florian; Hotho, Andreas R. West, Zia, L., Taraborelli, D., Leskovec, J. (Hrsg.) (2016).
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.
Semantics of User Interaction in Social Media.Mitzlaff, Folke; Atzmueller, Martin; Stumme, Gerd; Hotho, Andreas in Complex Networks IV, G. Ghoshal, Poncela-Casasnovas, J., Tolksdorf, R. (Hrsg.) (2013). (Bd. 476)
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, P. Serdyukov, Braslavski, P., Kuznetsov, S. O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (Hrsg.) (2013). (Bd. 7814) 86-97.
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
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) (2013). 9(4) 41--70.
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.
Computing semantic relatedness from human navigational paths on Wikipedia.Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas in WWW '13 Companion, ACM (Hrsg.) (2013). 171--172.
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.
Towards Mining Semantic Maturity in Social Bookmarking Systems.Atzmueller, Martin; Benz, Dominik; Hotho, Andreas; Stumme, Gerd A. Passant, Fernández, S., Breslin, J., Bojārs, U. (Hrsg.) (2011).
On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission).Mitzlaff, Folke; Atzmueller, Martin; Stumme, Gerd; Hotho, Andreas (2011).
From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research.Hotho, Andreas; Stumme, Gerd D. Fensel (Hrsg.) (2011). 143-153.
Combining Data-Driven and Semantic Approaches for Text Mining.Bloehdorn, Stephan; Blohm, Sebastian; Cimiano, Philipp; Giesbrecht, Eugenie; Hotho, Andreas; Lösch, Uta; Mädche, Alexander; Mönch, Eddie; Sorg, Philipp; Staab, Steffen; Völker, Johanna D. Fensel (Hrsg.) (2011). 115-142.
Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity.Körner, Christian; Benz, Dominik; Strohmaier, Markus; Hotho, Andreas; Stumme, Gerd (2010).
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.
Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge.Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2010).
Query Logs as Folksonomies.Benz, Dominik; Hotho, Andreas; Jäschke, Robert; Krause, Beate; Stumme, Gerd in Datenbank-Spektrum (2010). 10(1) 15--24.
Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.
Data Mining on Folksonomies.Hotho, Andreas in Intelligent Information Access, G. Armano, de Gemmis, M., Semeraro, G., Vargiu, E. (Hrsg.) (2010). (Bd. 301) 57-82.
Social resource sharing systems are central elements of the Web 2.0 and use all the same kind of lightweight knowledge representation, called folksonomy. As these systems are easy to use, they attract huge masses of users. Data Mining provides methods to analyze data and to learn models which can be used to support users. The application and adaptation of known data mining algorithms to folksonomies with the goal to support the users of such systems and to extract valuable information with a special focus on the Semantic Web is the main target of this paper. In this work we give a short introduction into folksonomies with a focus on our own system BibSonomy. Based on the analysis we made on a large folksonomy dataset, we present the application of data mining algorithms on three different tasks, namely spam detection, ranking and recommendation. To bridge the gap between folksonomies and the Semantic Web, we apply association rule mining to extract relations and present a deeper analysis of statistical measures which can be used to extract tag relations. This approach is complemented by presenting two approaches to extract conceptualizations from folksonomies.
Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0.Berendt, Bettina; Hotho, Andreas; Stumme, Gerd in Web Semantics: Science, Services and Agents on the World Wide Web (2010). 8(2-3) 95 - 96.
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems.Cattuto, Ciro; Benz, Dominik; Hotho, Andreas; Stumme, Gerd in Lecture Notes in Computer Science (2008). (Bd. 5318) 615--631.
Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.
Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems.Cattuto, Ciro; Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2008).
Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.
Discovering Shared Conceptualizations in Folksonomies.Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph; Ganter, Bernhard; Stumme, Gerd in Web Semantics: Science, Services and Agents on the World Wide Web (2008). 6(1) 38--53.
Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.
Analyzing Tag Semantics Across Collaborative Tagging Systems.Benz, Dominik; Grobelnik, Marko; Hotho, Andreas; Jäschke, Robert; Mladenic, Dunja; Servedio, Vito D. P.; Sizov, Sergej; Szomszor, Martin in Dagstuhl Seminar Proceedings, H. Alani, Staab, S., Stumme, G. (Hrsg.) (2008).
The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.
AEON - An approach to the automatic evaluation of ontologies.Völker, Johanna; Vrandečić, Denny; Sure, York; Hotho, Andreas in Applied Ontology (2008). 3(1-2) 41--62.
OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.
Position Paper: Ontology Learning from Folksonomies.Benz, Dominik; Hotho, Andreas A. Hinneburg (Hrsg.) (2007). 109-112.
Network Properties of Folksonomies.Schmitz, Christoph; Grahl, Miranda; Hotho, Andreas; Stumme, Gerd; Catutto, Ciro; Baldassarri, Andrea; Loreto, Vittorio; Servedio, Vito D. P. (2007).
Network Properties of Folksonomies.Cattuto, Ciro; Schmitz, Christoph; Baldassarri, Andrea; Servedio, Vito D. P.; Loreto, Vittorio; Hotho, Andreas; Grahl, Miranda; Stumme, Gerd in AI Communications (2007). 20(4) 245 - 262.
Learning Disjointness.Völker, Johanna; Vrandecic, Denny; Sure, York; Hotho, Andreas in Lecture Notes in Computer Science, E. Franconi, Kifer, M., May, W. (Hrsg.) (2007). (Bd. 4519)
Semantics, Web and MiningAckermann, Markus; Berendt, Bettina; Grobelnik, Marko; Hotho, Andreas; Mladenic, Dunja; Semeraro, Giovanni; Spiliopoulou, Myra; Stumme, Gerd; Svatek, Vojtech; van Someren, Maarten (2006).
Semantic Web Mining - State of the Art and Future Directions.Stumme, Gerd; Hotho, Andreas; Berendt, Bettina in Journal of Web Semantics (2006). 4(2) 124-143.
SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining. This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on improving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.
Semantic Network Analysis of Ontologies.Hoser, Bettina; Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd in LNCS (2006). (Bd. 4011) 514-529.
Mining Association Rules in Folksonomies.Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd in Studies in Classification, Data Analysis, and Knowledge Organization, V. Batagelj, Bock, H. -H., Ferligoj, A., Žiberna, A. (Hrsg.) (2006). 261-270.
Learning Ontologies to Improve Text Clustering and Classification.Bloehdorn, Stephan; Cimiano, Philipp; Hotho, Andreas in From Data and Information Analysis to Knowledge Engineering (2006). 334--341.
Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones. ER -
Kollaboratives Wissensmanagement.Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd in Semantic Web - Wege zur vernetzten Wissensgesellschaft, T. Pellegrini, Blumauer, A. (Hrsg.) (2006). 273-290.
Wissensmanagement in zentralisierten Wissensbasen erfordert einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.
FolkRank: A Ranking Algorithm for Folksonomies.Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd (2006). 111-114.
In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006.
Emergent Semantics in BibSonomy.Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd (2006). (Bd. P-94)
Boosting for Text Classification with Semantic Features.Bloehdorn, Stephan; Hotho, Andreas in Advances in Web Mining and Web Usage Analysis (2006). (Bd. 3932) 149--166.
Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space.Berendt, Bettina; Hotho, Andreas; Stumme, Gerd V. Svatek, Snasel, V. (Hrsg.) (2005). 1--16.
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis.Cimiano, Philipp; Hotho, Andreas; Staab, Steffen in Journal on Artificial Intelligence Research (2005). 24 305-339.
An Ontology-based Framework for Text Mining.Bloehdorn, Stephan; Cimiano, Philipp; Hotho, Andreas; Staab, Steffen in LDV Forum - GLDV Journal for Computational Linguistics and Language Technology (2005). 20(1) 87-112.
Web Mining: From Web to Semantic WebBerendt, Bettina; Hotho, Andreas; Mladenic, Dunja; van Someren, Maarten; Spiliopoulou, Myra; Stumme, Gerd in LNAI (2004). (Bd. 3209) Springer, Heidelberg.
Usage Mining for and on the Semantic Web.Berendt, B.; Hotho, A.; Stumme, G. in Data Mining Next Generation Challenges and Future Directions, H. Kargupta, Joshi, A., Sivakumar, K., Yesha, Y. (Hrsg.) (2004). 461-481.
Semantic Web PersonalizationMobasher, Bamshad; Anand, Sarabjot Singh; Berendt, Bettina; Hotho, Andreas (2004).
Learning Concept Hierarchies from Text Corpora using Formal Concept AnalysisCimiano, Philipp; Hotho, Andreas; Staab, Steffen (2004).
International Workshop on Mining for and from the Semantic Web (MSW2004)Hotho, Andreas; Sure, York; Getoor, Lise (2004).
Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies.Cimiano, Philipp; Hotho, Andreas; Stumme, Gerd; Tane, Julien in LNCS (2004). (Bd. 2961)
Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text.Cimiano, Philipp; Hotho, Andreas; Staab, Steffen R. L. de Mántaras, Saitta, L. (Hrsg.) (2004). 435-439.
Clustering Ontologies from Text.Cimiano, Philipp; Hotho, Andreas; Staab, Steffen (2004).
Boosting for Text Classification with Semantic Features.Bloehdorn, Stephan; Hotho, Andreas (2004). 70-87.
A workshop report: mining for and from the Semantic Web at KDD 2004.Hotho, Andreas; Sure, York; Getoor, Lise in SIGKDD Explorations (2004). 6(2) 142-143.
A roadmap for web mining: From web to semantic web.Berendt, Bettina; Hotho, Andreas; Mladenic, Dunja; Van Someren, Maarten; Spiliopoulou, Myra; Stumme, Gerd in Web Mining: From Web to Semantic Web (2004). 1--22.
Semantic Web - State of the art and future directions.Studer, Rudi; Volz, Raphael; Stumme, Gerd; Hotho, Andreas in KI Heft, Special Issue on the Semantic Web (2003). 3 5-9.
Ontology-based Text Document Clustering.Staab, Steffen; Hotho, Andreas (2003). 451-452.
Ontologies Improve Text Document Clustering.Hotho, A.; Staab, S.; Stumme, G. (2003). 541-544.
Explaining Text Clustering Results using Semantic Structures.Hotho, A.; Staab, S.; Stumme, G. in LNCS (2003). (Bd. 2838) 217-228.
Building and Using the Semantic Web.Studer, Rudi; Stumme, Gerd; Handschuh, Siegfried; Hotho, Andreas; Motik, Boris (2003). 31-34.
Usage Mining for and on the Semantic Web.Stumme, Gerd; Berendt, Bettina; Hotho, Andreas (2002). 77-86.
Towards Semantic Web Mining.Berendt, B.; Hotho, A.; Stumme, G. in Lecture Notes in Computer Science (LNCS), I. Horrocks, Hendler, J. A. (Hrsg.) (2002). (Bd. 2342) 264-278.
Semantic Web Mining for Building Information Portals (Position Paper).Hartmann, Jens; Hotho, Andreas; Stumme, Gerd (2002). 34-38.
Semantic Web MiningBerendt, B.; Hotho, A.; Stumme, G. (2002). Workshop at 13th Europ. Conf. on Machine Learning (ECML'02) / 6th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'02), Helsinki.
KAON - Towards a large scale Semantic Web.Ehrig, Marc; Handschuh, Siegfried; Hotho, Andreas; Maedche, Alexander; Motik, Boris; Oberle, Daniel; Schmitz, Christoph; Staab, Steffen; Stojanovic, Ljiljana; Stojanovic, Nenad; Studer, Rudi; Stumme, Gerd; Sure, York; Tane, Julien; Volz, Raphael; Zacharias, Valentin in LNCS, K. Bauknecht, Tjoa, A. M., Quirchmayr, G. (Hrsg.) (2002).
KAON - Towards a Large Scale Semantic Web.Bozsak, E.; Ehrig, M.; Handschuh, S.; Hotho, A.; Maedche, A.; Motik, B.; Oberle, D.; Schmitz, C.; Staab, S.; Stojanovic, L.; Stojanovic, N.; Studer, R.; Stumme, G.; Sure, Y.; Tane, J.; Volz, R.; Zacharias, V. in LNCS, K. Bauknecht, Tjoa, A. M., Quirchmayr, G. (Hrsg.) (2002). (Bd. 2455) 304--313.
Folksonomies. in Encyclopedia of Social Network Analysis and Mining (2014). 542--547.[ BibTeX ]
Towards Mining Semantic Maturity in Social Bookmarking Systems.A. Passant, Fernández, S., Breslin, J., Bojārs, U. (Hrsg.) (2011).
Query Logs as Folksonomies. in Datenbank-Spektrum (2010). 10(1) 15--24.
Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems. (2008).
Semantics, Web and Mining (2006).
Semantic Web Personalization (2004).[ BibTeX ]
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis (2004).
