A Fuzzy Algorithm for Optimizing Semantic Documental Searches

Abstract

Search for documents is a common and pertinent task lots of organizations face every day as well as common Internet users in their daily searches. One specific document search is scientific paper search in reference manager systems such as Mendeley or IEEExplore. Considering the difficult task finding documents can sometimes represent, semantic search is currently being applied to improve this type of search. As the act of deciding if a document is a good result for a given search expression is vague, fuzziness becomes an important aspect when defining search algorithms. In this paper, the author present a fuzzy algorithm for improving documental searches optimized for specific scenarios where we want to find a document but don´t remember the exact words used, if plural or singular words were used or if a synonym was used. The author also present the application of this algorithm to a real scenario comparing to Mendeley and IEEExplore results.

Juvenile‐onset and adult‐onset demodicosis in dogs in the UK: prevalence and breed associations

“…These cases could have been first diagnosed during 2013 (incident cases) or ongoing cases first diagnosed before 2013 (pre-existing cases). Case-finding involved initial screening of all 455,553 study dogs for candidate demodicosis cases by searching the clinical free-text field using the search term "demod*" with fuzziness to allow two-letter inversion, insertion or deletion (Paiva 2013). Candidate cases were randomly ordered and the clinical notes of all 3307 candidate animals were manually reviewed in detail to evaluate them for case inclusion.…”