Mohammad, S. & Hirst, G. (Submitted for publication), 'Distributional measures as proxies for semantic relatedness', .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Berendt, B.; Hotho, A. & Stumme, G. (2010), 'Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0', Web Semantics: Science, Services and Agents on the World Wide Web 8 (2-3) , 95 - 96 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Hotho, A.; Benz, D.; Eisterlehner, F.; Jäschke, R.; Krause, B.; Schmitz, C. & Stumme, G. (2010), 'Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System für Wissenschaftler', HMD -- Praxis der Wirtschaftsinformatik Heft 271 , 47-58 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.

Körner, C.; Benz, D.; Strohmaier, M.; Hotho, A. & Stumme, G. (2010), Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity, in 'Proceedings of the 19th International World Wide Web Conference (WWW 2010)' , ACM, Raleigh, NC, USA .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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.

Mitzlaff, F.; Benz, D.; Stumme, G. & Hotho, A. (2010), Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy, in 'Proceedings of the 21st ACM conference on Hypertext and hypermedia' .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2009

Benz, D.; Krause, B.; Kumar, G. P.; Hotho, A. & Stumme, G. (2009), Characterizing Semantic Relatedness of Search Query Terms, in 'Proceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)' .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2008

Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008), Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems, in 'Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)' .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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.

Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008), Semantic Grounding of Tag Relatedness in Social Bookmarking Systems, in Amit Sheth; Steffen Staab; Mike Dean; Massimo Paolucci; Diana Maynard; Timothy Finin & Krishnaprasad Thirunarayan, ed., 'The Semantic Web - ISWC 2008' , Springer Berlin / Heidelberg, , pp. 615--631 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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. Even though most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptions on the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity in terms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures of tag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding is provided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measures of semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of the investigated similarity measures and indicates which ones are better suited in the context of a given semantic application.

Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008), Semantic Grounding of Tag Relatedness in Social Bookmarking Systems, in Amit P. Sheth; Steffen Staab; Mike Dean; Massimo Paolucci; Diana Maynard; Timothy W. Finin & Krishnaprasad Thirunarayan, ed., 'The Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008' , Springer, Heidelberg , pp. 615--631 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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.

2006

Ackermann, M.; Berendt, B.; Grobelnik, M.; Hotho, A.; Mladenic, D.; Semeraro, G.; Spiliopoulou, M.; Stumme, G.; Svatek, V. & van Someren, M., ed. (2006), Semantics, Web and Mining , Springer , Heidelberg .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Alani, H.; Hoser, B.; Schmitz, C. & Stumme, G., ed. (2006), Proceedings of the 2nd Workshop on Semantic Network Analysis .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Budanitsky, A. & Hirst, G. (2006), 'Evaluating WordNet-based Measures of Lexical Semantic Relatedness', Computational Linguistics 32 (1) , 13-47 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Hoser, B.; Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006), Semantic Network Analysis of Ontologies, in York Sure & John Domingue, ed., 'The Semantic Web: Research and Applications' , Springer, Heidelberg , pp. 514-529 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

Novácek, V. & Smrz, P. (2006), Empirical Merging of Ontologies - A Proposal of Universal Uncertainty Representation Framework., in York Sure & John Domingue, ed., 'ESWC' , Springer, , pp. 65-79 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Pellegrini, T. & Blumauer, A., ed. (2006), Semantic Web: Wege zur vernetzten Wissensgesellschaft , Springer , Heidelberg .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G. (2006), Kollaboratives Wissensmanagement , Springer , pp. 273-290 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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.

Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G. (2006), Mining Association Rules in Folksonomies, in V. Batagelj; H.-H. Bock; A. Ferligoj & A. Žiberna, ed., 'Data Science and Classification. Proceedings of the 10th IFCS Conf.' , Springer, Heidelberg , pp. 261--270 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.

Serafini, L.; Zanobini, S.; Sceffer, S. & Bouquet, P. (2006), Matching Hierarchical Classifications with Attributes., in York Sure & John Domingue, ed., 'ESWC' , Springer, , pp. 4-18 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Strube, M. & Ponzetto, S. P. (2006), WikiRelate! computing semantic relatedness using wikipedia, in 'proceedings of the 21st national conference on Artificial intelligence - Volume 2' , AAAI Press, , pp. 1419--1424 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.

Stumme, G.; Hotho, A. & Berendt, B. (2006), 'Semantic Web Mining - State of the Art and Future Directions', Journal of Web Semantics 4 (2) , 124-143 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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.

Sure, Y. & Domingue, J., ed. (2006), The Semantic Web: Research and Applications, 3rd European
Semantic Web Conference, ESWC 2006, Budva, Montenegro, June
11-14, 2006, Proceedings , Vol. 4011 , Springer .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

van Assem, M.; Malaisé, Vé.; Miles, A. & Schreiber, G. (2006), A Method to Convert Thesauri to SKOS., in York Sure & John Domingue, ed., 'ESWC' , Springer, , pp. 95-109 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2005

Berendt, B.; Hotho, A. & Stumme, G. (2005), Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space, in Vojtech Svatek & Vaclav Snasel, ed., 'Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space' , Technical University of Ostrava, , pp. 1-16 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Stumme, G.; Hoser, B.; Schmitz, C. & Alani, H., ed. (2005), Proceedings of the First Workshop on Semantic Network Analysis , CEUR Proceedings , Aachen .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2004

Berendt, B.; Hotho, A.; Mladenic, D.; van Someren, M.; Spiliopoulou, M. & Stumme, G. (2004), A Roadmap for Web Mining: From Web to Semantic Web., in Bettina Berendt; Andreas Hotho; Dunja Mladenic; Maarten van Someren; Myra Spiliopoulou & Gerd Stumme, ed., 'Web Mining: From Web to Semantic Web' , Springer, Heidelberg , pp. 1-22 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

The purpose of Web mining is to develop methods and systems for discovering models of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining (we use this term here also for the closely related areas of Machine Learning and Knowledge Discovery), Internet technology and World Wide Web, and for the more recent Semantic Web. The World Wide Web has made an enormous amount of information electronically accessible. The use of email, news and markup languages like HTML allow users to publish and read documents at a world-wide scale and to communicate via chat connections, including information in the form of images and voice records. The HTTP protocol that enables access to documents over the network via Web browsers created an immense improvement in communication and access to information. For some years these possibilities were used mostly in the scientific world but recent years have seen an immense growth in popularity, supported by the wide availability of computers and broadband communication. The use of the internet for other tasks than finding information and direct communication is increasing, as can be seen from the interest in ldquoe-activitiesrdquo such as e-commerce, e-learning, e-government, e-science.

Berendt, B.; Hotho, A. & Stumme, G. (2004), Usage Mining for and on the Semantic Web, in Hillol Kargupta; Anupam Joshi; Krishnamoorthy Sivakumar & Yelena Yesha, ed., 'Data Mining Next Generation Challenges and Future Directions' , AAAI Press, Boston , pp. 461-481 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of data Web mining operates on, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web resources and navigation behavior are increasingly being used. This fits exactly with the aims of the Semantic Web: the Semantic Web enriches the WWW by machine-processable information which supports the user in his tasks. In this paper, we discuss the interplay of the Semantic Web with Web Mining, with a specific focus on usage mining.

Berendt, B.; Hotho, A.; Mladenic, D.; van Someren, M.; Spiliopoulou, M. & Stumme, G., ed. (2004), Web Mining: From Web to Semantic Web, First European Web
Mining Forum, EMWF 2003, Cavtat-Dubrovnik, Croatia, September
22, 2003, Revised Selected and Invited Papers , Vol. 3209 , Springer , Heidelberg .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Tane, J.; Schmitz, C. & Stumme, G. (2004), Semantic resource management for the web: an e-learning application, in 'Proc. 13th International World Wide Web Conference (WWW 2004)' , pp. 1-10 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2003

Agarwal, S.; Fankhauser, P.; Gonzalez-Ollala, J.; Hartmann, J.; Hollfelder, S.; Jameson, A.; Klink, S.; Lehti, P.; Ley, M.; Rabbidge, E.; Schwarzkopf, E.; Shrestha, N.; Stojanovic, N.; Studer, R.; Stumme, G. & Walter, B. (2003), Semantic Methods and Tools for Information Portals, in K. Dittrich; W. König; A. Oberweis; K. Rannenberg & W. Wahlster, ed., 'INFORMATIK 2003 -- Innovative Informatikanwendungen (Band 1)' , Gesellschaft für Informatik, Bonn , pp. 116-131 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

The paper describes a set of approaches for representing and accessing information within a semantically structured information portal, while offering the possibility to integrate own information. It discusses research performed within the project `Semantic Methods and Tools for Information Portals (SemIPort)'. In particular, it focuses on (1) the development of scalable storing, processing and querying methods for semantic data, (2) visualization and browsing of complex data inventories, (3) personalization and agent-based interaction, and (4) the enhancement of web mining approaches for use within a semantics-based portal.

Hotho, A.; Staab, S. & Stumme, G. (2003), Explaining Text Clustering Results using Semantic Structures, in Nada Lavrač; Dragan Gamberger & Hendrik BlockeelLjupco Todorovski, ed., 'Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases' , Springer, Heidelberg , pp. 217-228 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Common text clustering techniques offer rather poor capabilities for explaining to their users why a particular result has been achieved. They have the disadvantage that they do not relate semantically nearby terms and that they cannot explain how resulting clusters are related to each other. In this paper, we discuss a way of integrating a large thesaurus and the computation of lattices of resulting clusters into common text clustering in order to overcome these two problems. As its major result, our approach achieves an explanation using an appropriate level of granularity at the concept level as well as an appropriate size and complexity of the explaining lattice of resulting clusters.

Hotho, A.; Staab, S. & Stumme, G. (2003), 'Text Clustering Based on Background Knowledge' , Technical report, University of Karlsruhe, Institute AIFB .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. Standard partitional or agglomerative clustering methods efficiently compute results to this end. However, the bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning has been achieved, because it is only specified extensionally. In order to deal with the two problems, we integrate background knowledge into the process of clustering text documents. First, we preprocess the texts, enriching their representations by background knowledge provided in a core ontology — in our application Wordnet. Then, we cluster the documents by a partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks. Second, the clustering partitions the large number of documents to a relatively small number of clusters, which may then be analyzed by conceptual clustering. In our approach, we applied Formal Concept Analysis. Conceptual clustering techniques are known to be too slow for directly clustering several hundreds of documents, but they give an intensional account of cluster results. They allow for a concise description of commonalities and distinctions of different clusters. With background knowledge they even find abstractions like “food” (vs. specializations like “beef” or “corn”). Thus, in our approach, partitional clustering reduces first the size of the problem such that it becomes tractable for conceptual clustering, which then facilitates the understanding of the results.

Studer, R.; Stumme, G.; Handschuh, S.; Hotho, A. & Motik, B. (2003), Building and Using the Semantic Web, in 'New Trends in Knowledge Processing -- Data Mining, Semantic Web and Computational' , pp. 31-34 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2002

Berendt, B.; Hotho, A. & Stumme, G., ed. (2002), Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf. on
Machine Learning (ECML'02) / 6th Europ. Conf. on Principles and
Practice of Knowledge Discovery in Databases (PKDD'02) , Helsinki, Finland .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Berendt, B.; Hotho, A. & Stumme, G. (2002), Towards Semantic Web Mining, in I. Horrocks & J. Hendler, ed., 'The Semantic Web -- ISWC 2002' , Springer, Heidelberg , pp. 264-278 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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. (2002), KAON - Towards a large scale Semantic Web, in Kurt Bauknecht; A. Min Tjoa & Gerald Quirchmayr, ed., 'Proceedings of the Third International Conference on E-Commerce and Web Technologies (EC-Web 2002), Aix-en-Provence, France' , Springer, , pp. 304-313 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Gonzalez-Olalla, J. & Stumme, G. (2002), Semantic Methods and Tools for Information Portals - The SemIPort Project (Project Description), in B. Berendt; A. Hotho & G. Stumme, ed., 'Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf.' , pp. 90 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Hartmann, J.; Hotho, A. & Stumme, G. (2002), Semantic Web Mining for Building Information Portals (Position Paper), in 'Proc. Arbeitskreistreffen Knowledge Discovery' .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Schmitz, C.; Staab, S.; Studer, R.; Stumme, G. & Tane, J. (2002), Accessing Distributed Learning Repositories through a Courseware
Watchdog, in M. Driscoll & T.C. Reeves, ed., 'Proc. of E-Learning 2002 World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education on (E-Learning 2002)' , pp. 909-915 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Stumme, G.; Berendt, B. & Hotho, A. (2002), Usage Mining for and on the Semantic Web, in 'Proc. NSF Workshop on Next Generation Data Mining' , pp. 77-86 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Stumme, G. (2002), Using Ontologies and Formal Concept Analysis for Organizing Business Knowledge, in J. Becker & R. Knackstedt, ed., 'Wissensmanagement mit Referenzmodellen -- Konzepte für die Anwendungssystem- und Organisationsgestaltung' , Physica, Heidelberg , pp. 163-174 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2001

Mcdonald, S. & Ramscar, M. (2001), Testing the distributional hypothesis: The influence of context on judgements of semantic similarity, in 'In Proceedings of the 23rd Annual Conference of the Cognitive Science Society' , pp. 611--6 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Distributional information has recently been implicated as playing an important role in several aspects of language ability. Learning the meaning of a word is thought to be dependent, at least in part, on exposure to the word in its linguistic contexts of use. In two experiments, we manipulated subjects ’ contextual experience with marginally familiar and nonce words. Results showed that similarity judgements involving these words were affected by the distributional properties of the contexts in which they were read. The accrual of contextual experience was simulated in a semantic space model, by successively adding larger amounts of experience in the form of item-in-context exemplars sampled from the British National Corpus. The experiments and the simulation

Stumme, G. & Maedche, A. (2001), FCA-Merge: Bottom-Up Merging of Ontologies., in B. Nebel, ed., 'Proc. 17th Intl. Conf. on Artificial Intelligence (IJCAI '01)' , pp. 225-230 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2000

Stumme, G.; Studer, R. & Sure, Y. (2000), Towards an Order-Theoretical Foundation for Maintaining and Merging Ontologies, in F. Bodendorf & M. Grauer, ed., 'Verbundtagung Wirtschaftsinformatik 2000' , Shaker, Aachen , pp. 136-149 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

1997

Hirst, G. & St-Onge, D. (1997), 'Lexical Chains as representation of context for the detection and correction
malapropisms' .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

1995

Resnik, P. (1995), Using Information Content to Evaluate Semantic Similarity in a Taxonomy, in 'Proceedings of the XI International Joint Conferences on Artificial' , pp. 448-453 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]