A
Andrew MacFarlane
Researcher at City University London
Publications - 92
Citations - 1512
Andrew MacFarlane is an academic researcher from City University London. The author has contributed to research in topics: Information seeking & Search engine indexing. The author has an hindex of 18, co-authored 88 publications receiving 1402 citations. Previous affiliations of Andrew MacFarlane include Microsoft & Queen Mary University of London.
Papers
More filters
Journal ArticleDOI
A review of ontology based query expansion
TL;DR: The meaning of context in relation to ontology based query expansion is examined and a review of query expansion approaches including relevance feedback, corpus dependent knowledge models and corpus independent knowledge models are included.
Journal ArticleDOI
Geographic information retrieval in a mobile environment: evaluating the needs of mobile individuals
David Mountain,Andrew MacFarlane +1 more
TL;DR: The results of evaluation suggest that retrieved information to which post-query geographic filters have been applied is considered morerelevant than unfiltered information, and that users find information sorted by spatial proximity to be more relevant than that sorted by a prediction surface of likely future locations.
Proceedings ArticleDOI
Parallel search using partitioned inverted files
TL;DR: This workamines the searching of partitioned inverted files with particular emphasis on issues that arise from different types of partitioning methods, and concludes that the DocId method is the best.
Book ChapterDOI
Field-weighted XML retrieval based on BM25
TL;DR: In the first year of INEX 2004, the Centre for Interactive Systems Research (CISR) participated in the INEX competition and proposed a field-weighted BM25F for document retrieval to element level retrieval function BM25E.
Journal Article
Field-weighted XML retrieval based on BM25
TL;DR: This paper introduces a newly developed XML indexing and retrieval system on Okapi and extends Robertson’s field-weighted BM25F for document retrieval to element level retrieval function BM25E, and shows how the tuned weights for selected fields are tuned by using INEX 2004 topics and assessments.