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A Ranking Algorithm for the Detection of Composite Concepts Based on Multiple Taxonomies

Authors:
Daniel Kimmig
Steffen Scholz
Andreas Schmidt

Keywords: ranking algorithm; taxonomy based search; simil- larity function; performance measure

Abstract:
A full-text search is typically not appropriate for concept mining. For that reason, we use taxonomies to describe the concepts we are looking for. A typical input for our search consists of two or more taxonomies, describing the concept we are looking for. In this paper, we present a similarity measure between the input taxonomies and the searched documents. The algorithm is based on the idea of word n-tuples, where each word in a result tuple comes from another taxonomy. Because of the vast number of available documents, our similarity function must be fast to allow a quick ranking of the retrieved documents. We also provide an optimized implementation for our algorithm, which allows a fast ranking of the searched documents.

Pages: 72 to 75

Copyright: Copyright (c) IARIA, 2013

Publication date: November 17, 2013

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-311-7

Location: Lisbon, Portugal

Dates: from November 17, 2013 to November 21, 2013