EPOS : evolving personal to organizational knowledge spaces

EPOS will leverage the user´s personal workspace with its manyfold native information structures to his personal knowledge space and in cooperation with other personal workspaces contribute to the organizational knowledge space which is represented in the organizational memory. This first milestone presents results from the project´s first year in the areas of the personal informational model, user observation for context elicitation, collaborative information retrieval and information visualization

EPOS : evolving personal to organizational knowledge spaces

EPOS will leverage the user´s personal workspace with its manyfold native information structures to his personal knowledge space and in cooperation with other personal workspaces contribute to the organizational knowledge space which is represented in the organizational memory. This first milestone presents results from the project´s first year in the areas of the personal informational model, user observation for context elicitation, collaborative information retrieval and information visualization

Towards Collaborative Information Retrieval

Information Retrieval Systems have been studied in Computer Science for Decades. The traditional ad-hoc task in Information Retrieval is to find all documents relevant for an ad-hoc given query. Much work has been spent into improving on this task, in particular in the Text Retrieval Evaluation Conference series (TREC). In 1998 it was decided on TREC-8 that this task should not be longer persuaded within TREC, in particular because the accuracy has plateaued in the last years. At DFKI, we are working on approaches for Collaborative Information Retrieval (CIR) which learn to improve retrieval e#ectiveness from the interaction of di#erent users with the retrieval engine. Such systems may have the potential to overcome the current plateau in ad-hoc retrieval

1 Towards Collaborative Information Retrieval: Three Approaches

Abstract. The accuracy of ad-hoc document retrieval systems has plateaued in the last few years. At DFKI, we are working on so-called collaborative information retrieval (CIR) systems which unintrusively learn from their users ’ search processes. As a first step towards techniques, we focus on a restricted setting in CIR in which only old queries and correct answer documents to these queries are available for improving on a new query. For this restricted setting we propose three initial approaches, called QSD, QLD, and TCL as well as combinations of these approaches with pseudo relevance feedback. The approaches are evaluated experimentally on standard Information Retrieval test collections. It turns out that in particular the hybrid approaches with pseudo relevance feedback give promising results. A bigger advantage of the proposed approaches is expected in real word test scenarios in which the overlap of user interests is larger than in our experimental set up. 1.

Query Reformulation in Collaborative Information Retrieval

Information retrieval (IR) systems utilize user feedback for generating optimal queries with respect to a particular information need. However the methods that have been developed in IR for generating these queries do not memorize information gathered from previous search processes, and hence can not use such information in new search processes. Thus each new search process does not know anything about previous search processes and can not prot from the results of the previous processes. We call systems which can consider results from previous search processes Collaborative Information Retrieval (CIR) systems. Improving retrieval quality in a CIR system should be possible, since the system can learn from many queries issued from various users. In this paper we present a new method for use in CIR. We are proposing to use previously learned queries and their relevant documents for improving overall retrieval quality. Based on the similarity of a new query to previously learned queries we are expanding the new query by extracting terms from documents which have been judged as relevant to these previously learned queries. Thus our method uses global feedback information for query expansion in contrast to local feedback information which has been used in previous work in query expansion methods

Query Reformulation in Collaborative Information Retrieval

Information retrieval (IR) systems utilize user feedback for generating optimal queries with respect to a particular information need. However the methods that have been developed in IR for generating these queries do not memorize information gathered from previous search processes, and hence can not use such information in new search processes. Thus each new search process does not know anything about previous search processes and can not profit from the results of the previous processes. We call systems which can consider results from previous search processes Collaborative Information Retrieval (CIR) systems. Improving retrieval quality in a CIR system should be possible, since the system can learn from many queries issued from various users. In this paper we present a new method for use in CIR. We are proposing to use previously learned queries and their relevant documents for improving overall retrieval quality. Based on the similarity of a new query to previously learned queries we are expanding the new query by extracting terms from documents which have been judged as relevant to these previously learned queries. Thus our method uses global feedback information for query expansion in contrast to local feedback information which has been used in previous work in query expansion methods