Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G.: Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems. Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3). Patras, Greece: 2008, S. 39-43
[Volltext]
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.

Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G.: Tag Recommendations in Social Bookmarking Systems. In: AI Communications 21 (2008), Nr. 4, S. 231-247
[Volltext]
Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

Völker, J.; Vrandečić, D.; Sure, Y. & Hotho, A.: AEON - An approach to the automatic evaluation of ontologies. In: Applied Ontology 3 (2008), Nr. 1-2, S. 41-62
[Volltext]
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.

Benz, D. & Hotho, A.: Position Paper: Ontology Learning from Folksonomies. In: Hinneburg, A. (Hrsg.): Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Halle/Saale: Martin-Luther-Universität Halle-Wittenberg, 2007, S. 109-112
[Volltext]
The emergence of collaborative tagging systems with their underlying flat and
controlled resource organization paradigm has led to a large number of
search activities focussing on a formal description and analysis of the
sulting "folksonomies". An interesting outcome is that the characteristic
alities of these systems seem to be inverse to more traditional knowledge
ructuring approaches like taxonomies or ontologies: The latter provide rich
d precise semantics, but suffer - amongst others - from a knowledge
quisition bottleneck. An important step towards exploiting the possible
nergies by bridging the gap between both paradigms is the automatic
traction of relations between tags in a folksonomy. This position paper
esents preliminary results of ongoing work to induce hierarchical
lationships among tags by analyzing the aggregated data of collaborative
gging systems as a basis for an ontology learning procedure.

Romero, C. & Ventura, S.: Educational data mining: A survey from 1995 to 2005. In: Expert Syst. Appl. 33 (2007), Nr. 1, S. 135-146
[Volltext]
Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.

Chen, C.-M.; Lee, H.-M. & Chen, Y.-H.: Personalized e-learning system using Item Response Theory. In: Comput. Educ. 44 (2005), Nr. 3, S. 237-255
[Volltext]
Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too many hyperlink structures in Web-based learning systems place a large information burden on learners. Consequently, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of an adaptive mechanism, and information overload are the main research issues. This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners. The item characteristic function proposed by Rasch with a single difficulty parameter is used to model the course materials. To obtain more precise estimation of learner ability, the maximum likelihood estimation (MLE) is applied to estimate learner ability based on explicit learner feedback. Moreover, to determine an appropriate level of difficulty parameter for the course materials, this study also proposes a collaborative voting approach for adjusting course material difficulty. Experiment results show that applying Item Response Theory (IRT) to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.

Rafaeli, S.; Dan-Gur, Y. & Barak, M.: Social Recommender Systems: Recommendations in Support of E-Learning. In: International Journal of Distance Education Technologies 3 (2005), Nr. 2, S. 30-47
[Volltext]
Recommendation systems can play an extensive role in online learning. In such systems, learners can receive guidance in locating and ranking references, knowledge bits, test items, and so forth. In recommender systems, users’ ratings can be applied toward items, users, other users’ ratings, and, if allowed, raters of raters of items recursively. In this chapter, we describe an online learning system — QSIA — an active recommender system for Questions Sharing and Interactive Assignments, designed to enhance knowledge sharing among learners. First, we lay out some of the theoretical background for social, open-rating mechanisms in online learning systems. We discuss concepts such as social versus black-box recommendations and the advice of neighbors as opposed to that of friends. We argue that enabling subjective views and ratings of other users is an inevitable phase of social collaboration systems. We also argue that social recommendations are critical for the exploitation of the value associated with recommendation.

Tang, T. Y. & Mccalla, G.: Smart Recommendation for an Evolving E-Learning System. Workshop on Technologies for Electronic Documents for Supporting Learning, International Conference on Artificial Intelligence in Education (AIED. 2003
[Volltext]

Middleton, S. E.; Alani, H. & Roure, D. C. D.: Exploiting Synergy Between Ontologies and Recommender Systems. Proceedings of the WWW2002 International Workshop on the Semantic Web. 2002
[Volltext]
Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.

Zaiane, O.: Building a recommender agent for e-learning systems. Proceedings of the International Conference on Computers in Education. Washington, DC, USA : 2002 (1), S. 55-59
[Volltext]
A recommender system in an e-learning context is a software agent that tries to "intelligently" recommend actions to a learner based on the actions of previous learners. This recommendation could be an on-line activity such as doing an exercise, reading posted messages on a conferencing system, or running an on-line simulation, or could be simply a web resource. These recommendation systems have been tried in e-commerce to entice purchasing of goods, but haven't been tried in e-learning. This paper suggests the use of web mining techniques to build such an agent that could recommend on-line learning activities or shortcuts in a course web site based on learners' access history to improve course material navigation as well as assist the online learning process. These techniques are considered integrated web mining as opposed to off-line web mining used by expert users to discover on-line access patterns.