Korinna Bade

Personalized Hierarchical Clustering

A hierarchical structure can provide efficient access to information contained in a collection of documents. However, such a structure is not always available, e.g. for a set of documents a user has collected over time in a single folder or the results of a Web search. We therefore investigate in this paper how we can obtain a hierarchical structure automatically, taking into account some background knowledge about the way a specific user would structure the collection. More specifically, we adapt a hierarchical agglomerative clustering algorithm to take into account user specific constraints on the clustering process. Such an algorithm could be applied, e.g., for user specific clustering of Web search results, where the users constraints on the clustering process are given by a hierarchical folder or bookmark structure. Besides the discussion of the algorithm itself, we motivate application scenarios and present an evaluation of the proposed algorithm on benchmark data

CARSA – an architecture for the development of context adaptive retrieval systems

Searching the Web and other local resources has become an every day task for almost everybody. However, the currently available tools for searching still provide only very limited support with respect to categorization and visualization of search results as well as personalization. In this paper, we present a system for searching that can be used by an end user and also by researchers in order to develop and evaluate a variety of methods to support a user in searching. The CARSA system provides a very flexible architecture based on web services and XML. This includes the use of different search engines, categorization methods, visualization techniques, and user interfaces. The user has complete control about the features used. This system therefore provides a platform for evaluating the usefulness of different retrieval support methods and their combination.

User Oriented Hierarchical Information Organization and Retrieval

In order to organize huge document collections, labeled hierarchical structures are used frequently. Users are most efficient in navigating such hierarchies, if they reflect their personal interests. Thus, we propose in this article an approach that is able to derive a personalized hierarchical structure from a document collection. The approach is based on a semi-supervised hierarchical clustering approach, which is combined with a biased cluster extraction process. Furthermore, we label the clusters for efficient navigation. Besides the algorithms itself, we describe an evaluation of our approach using benchmark datasets.

Evaluation Strategies for Learning Algorithms of Hierarchies

Several learning tasks comprise hierarchies. Comparison with a “gold-standard” is often performed to evaluate the quality of a learned hierarchy. We assembled various similarity metrics that have been proposed in different disciplines and compared them in a unified interdisciplinary framework for hierarchical evaluation which is based on the distinction of three fundamental dimensions. Identifying deficiencies for measuring structural similarity, we suggest three new measures for this purpose, either extending existing ones or based on new ideas. Experiments with an artificial dataset were performed to compare the different measures. As shown by our results, the measures vary greatly in their properties.

Hierarchical constraints

Constrained clustering received a lot of attention in the last years. However, the widely used pairwise constraints are not generally applicable for hierarchical clustering, where the goal is to derive a cluster hierarchy instead of a flat partition. Therefore, we propose for the hierarchical setting—based on the ideas of pairwise constraints—the use of must-link-before (MLB) constraints. In this paper, we discuss their properties and present an algorithm that is able to create a hierarchy by considering these constraints directly. Furthermore, we propose an efficient data structure for its implementation and evaluate its effectiveness with different datasets in a text clustering scenario.

Collection Browsing through Automatic Hierarchical Tagging

In order to navigate huge document collections efficiently, tagged hierarchical structures can be used. For users, it is important to correctly interpret tag combinations. In this paper, we propose the usage of tag groups for addressing this issue and an algorithm that is able to extract these automatically for text documents. The approach is based on the diversity of content in a document collection. For evaluation, we use methods from ontology evaluation and showed the validity of our approach on a benchmark dataset.

Rearranging Classified Items in Hierarchies Using Categorization Uncertainty

The classification into hierarchical structures is a problem of increasing importance, e.g. considering the growing use of ontologies or keyword hierarchies used in many web-based information systems. Therefore, it is not surprising that it is a field of ongoing research. Here, we propose an approach that utilizes hierarchy information in the classification process. In contrast to other methods, the hierarchy information is used independently of the classifier rather than integrating it directly. This enables the use of arbitrary standard classification methods. Furthermore, we discuss how hierarchical classification in general and our setting in specific can be evaluated appropriately. We present our algorithm and evaluate it on two datasets of web pages using Naive Bayes and SVM as baseline classifiers.

Hierarchical Classification by Expected Utility Maximization

Hierarchical classification refers to an extension of the standard classification problem, in which labels must be chosen from a class hierarchy. In this paper, we look at hierarchical classification from an information retrieval point of view. More specifically, we consider a scenario in which a user searches a document in a topic hierarchy. This scenario gives rise to the problem of predicting an optimal entry point, that is, a topic node in which the user starts searching. The usefulness of a corresponding prediction strongly depends on the search behavior of the user, which becomes relevant if the document is not immediately found in the predicted node. Typically, users tend to browse the hierarchy in a top-down manner, i.e., they look at a few more specific subcategories but usually refuse exploring completely different branches of the search tree. From a classification point of view, this means that a prediction should be evaluated, not solely on the basis of its correctness, but rather by judging its usefulness against the background of the user behavior. The idea of this paper is to formalize hierarchical classification within a decision-theoretic framework which allows for modeling this usefulness in terms of a user-specific utility function. The prediction problem thus becomes a problem of expected utility maximization. Apart from its theoretical appeal, we provide first empirical results showing that the approach performs well in practice.