Abstract
Life science data sources represent a complex link-driven federation of publicly available Web accessible sources. A fundamental need for scientists today is the ability to completely explore all relationships between scientific classes, e.g., genes and citations, that may be retrieved from various data sources. A challenge to such exploration is that each path between data sources potentially has different domain specific semantics and yields different benefit to the scientist. Thus, it is important to efficiently explore paths so as to generate paths with the highest benefits. In this paper, we explore the search space of paths that satisfy queries expressed as regular expressions. We propose an algorithm ESearch that runs in polynomial time in the size of the graph when the graph is acyclic. We present expressions to determine the benefit of a path based on metadata (statistics). We develop a heuristic search OnlyBestXX%. Finally, we compare OnlyBestXX% and ESearch.
This research has been partially supported by the NIH National Institute on Aging under grant number 1 R03 AG21671-01 and the National Science Foundation under grants IIS0219909 and EIA0130422.
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Lacroix, Z., Raschid, L., Vidal, ME. (2004). Efficient Techniques to Explore and Rank Paths in Life Science Data Sources. In: Rahm, E. (eds) Data Integration in the Life Sciences. DILS 2004. Lecture Notes in Computer Science(), vol 2994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24745-6_13
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DOI: https://doi.org/10.1007/978-3-540-24745-6_13
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