TY - GEN
T1 - Semantic ranking and result visualization for life sciences publications
AU - Stoyanovich, Julia
AU - Mee, William
AU - Ross, Kenneth A.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - An ever-increasing amount of data and semantic knowledge in the domain of life sciences is bringing about new data management challenges. In this paper we focus on adding the semantic dimension to literature search, a central task in scientific research. We focus our attention on PubMed, the most significant bibliographic source in life sciences, and explore ways to use high-quality semantic annotations from the MeSH vocabulary to rank search results. We start by developing several families of ranking functions that relate a search query to a document's annotations. We then propose an efficient adaptive ranking mechanism for each of the families. We also describe a two-dimensional Skyline-based visualization that can be used in conjunction with the ranking to further improve the user's interaction with the system, and demonstrate how such Skylines can be computed adaptively and efficiently. Finally, we evaluate the effectiveness of our ranking with a user study.
AB - An ever-increasing amount of data and semantic knowledge in the domain of life sciences is bringing about new data management challenges. In this paper we focus on adding the semantic dimension to literature search, a central task in scientific research. We focus our attention on PubMed, the most significant bibliographic source in life sciences, and explore ways to use high-quality semantic annotations from the MeSH vocabulary to rank search results. We start by developing several families of ranking functions that relate a search query to a document's annotations. We then propose an efficient adaptive ranking mechanism for each of the families. We also describe a two-dimensional Skyline-based visualization that can be used in conjunction with the ranking to further improve the user's interaction with the system, and demonstrate how such Skylines can be computed adaptively and efficiently. Finally, we evaluate the effectiveness of our ranking with a user study.
UR - http://www.scopus.com/inward/record.url?scp=77952773503&partnerID=8YFLogxK
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U2 - 10.1109/ICDE.2010.5447931
DO - 10.1109/ICDE.2010.5447931
M3 - Conference contribution
AN - SCOPUS:77952773503
SN - 9781424454440
T3 - Proceedings - International Conference on Data Engineering
SP - 860
EP - 871
BT - 26th IEEE International Conference on Data Engineering, ICDE 2010 - Conference Proceedings
T2 - 26th IEEE International Conference on Data Engineering, ICDE 2010
Y2 - 1 March 2010 through 6 March 2010
ER -