Ernesto William De Luca

Supporting information retrieval on mobile devices

We present a search interface that has been designed for the use on mobile devices in order to provide improved navigation and visualization of result sets. Besides automatic context switching, the interfaces provide methods to store prior search results in a structured way and to annotate documents with respect to user interests and the query. These methods enable a user to retrieve documents with fewer interactions and less data traffic, which is especially important for mobile devices.

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.

Using clustering methods to improve ontology-based query term disambiguation

In this article we describe results of our research on the disambiguation of user queries using ontologies for categorization. We present an approach to cluster search results by using classes or “Sense Folders” (prototype categories) derived from the concepts of an assigned ontology, in our case WordNet. Using the semantic relations provided from such a resource, we can assign categories to prior, not annotated documents. The disambiguation of query terms in documents with respect to a user‐specific ontology is an important issue in order to improve the retrieval performance for the user. Furthermore, we show that a clustering process can enhance the semantic classification of documents, and we discuss how this clustering process can be further enhanced using only the most descriptive classes of the ontology.

Adaptive support for cross-language text retrieval

In this paper we discuss ideas for adaptive support of cross-language text retrieval. In order to enable users to access multilingual information, different problems have to be solved: disambiguating and translating the query words, as well as categorizing and presenting the results appropriately. After giving a brief introduction to cross-language text retrieval, word sense disambiguation and document categorization, actual achievements of the research project are described and future work is discussed. We focus especially on the problem of browsing and navigation of the different word senses in a source and target languages.