Text-Based Information Retrieval Using Relevance Feedback
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Europeana, a freely accessible digital library with an idea to make Europe's cultural and scientific heritage available to the public was founded by the European Commission in 2008. The goal was to deliver a semantically enriched digital content with multilingual access to it. Even though they managed to increase the content of data they slowly faced the problem of retrieving information in an unstructured form. So to complement the Europeana portal services, ASSETS (Advanced Search Service and Enhanced Technological Solutions) was introduced with services that sought to improve the usability and accessibility of Europeana.
My contribution is to study different text-based information retrieval models, their relevance feedback techniques and to implement one simple model. The thesis explains a detailed overview of the information retrieval process along with the implementation of the chosen strategy for relevance feedback that generates automatic query expansion. Finally, the thesis concludes with the analysis made using relevance feedback, discussion on the model implemented and then an assessment on future use of this model both as a continuation of my work and using this model in ASSETS.
Place, publisher, year, edition, pages
2011. , 51 p.
Information Retrieval, Relevance Feedback, Query Expansion, Rocchio classification, Probabilistic model, Lucene, Similarity scoring function, Kullback-Leibler Divergence (KLD)
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-53603OAI: oai:DiVA.org:kth-53603DiVA: diva2:470416
Subject / course
Information and Software Systems
Master of Science - Software Engineering of Distributed Systems
Boman, Magnus, Professor