Investigating ontology based query expansion using a probabilistic retrieval model

This research briefly outlines the problems of traditional information retrieval systems and discusses the different approaches to inferring context in document retrieval. By context we mean word disambiguation which is achieved by exploring the generalisation-specialisation hierarchies within a given ontology. Specifically, we examine the use of ontology based query expansion for defining query context. Query expansion can be done in many ways and in this work we consider the use of relevance feedback and pseudo-relevance feedback for query expansion. We examine relevance feedback and pseudo-relevance to ascertain the existence of performance differences between relevance feedback and pseudo-relevance feedback. The information retrieval system used is based on the probabilistic retrieval model and the query expansion method is extended using information from a news domain ontology. The aim of this project is to assess the impact of the use of the ontology on the query expansion results. Our results show that ontology based query expansion has resulted in a higher number of relevant documents being retrieved compared to the standard relevance feedback process. Overall, ontology based query expansion improves recall but does not produce any significant improvements for the precision results. Pseudo-relevance feedback has achieved better results than relevance feedback. We also found that reducing or increasing the relevance feedback parameters (number of terms or number of documents) does not correlate with the results. When comparing the effect of varying the number of terms parameter with the number of documents parameter, the former benefits the pseudo-relevance feedback results but the latter has an additional effect on the relevance feedback results. There are many factors which influence the success of ontology based query expansion. The thesis discusses these factors and gives some guidelines on using ontologies for the purpose of query expansion

Evaluating the effectiveness of a collaborative querying environment

Collaborative querying seeks to help users formulate queries by sharing expert knowledge or other users’ search experiences. In previous work, a collaborative query environment (CQE) was developed for a digital library. The system operates by clustering and recommending related queries to users using a hybrid query similarity identification approach. Users can explore the query clusters using a graph-based visualization system known as the Query Graph Visualizer (QGV). The purpose of this paper is to evaluate the CQE with goal of informing the usefulness and usability of such a system. Our results show that compared with traditional information retrieval systems, collaborative querying can lead to faster information seeking when users perform unspecified tasks.Accepted versio

Evaluating the Effectiveness of a Collaborative Querying Environment

Abstract. Collaborative querying seeks to help users formulate queries by sharing expert knowledge or other users ’ search experiences. In previous work, a collaborative query environment (CQE) was developed for a digital library. The system operates by clustering and recommending related queries to users using a hybrid query similarity identification approach. Users can explore the query clusters using a graph-based visualization system known as the Query Graph Visualizer (QGV). The purpose of this paper is to evaluate the CQE with goal of informing the usefulness and usability of such a system. Our results show that compared with traditional information retrieval systems, collaborative querying can lead to faster information seeking when users perform unspecified tasks.