Top-k Ranked Document Search in General Text Databases

DOI: 10.1007/978-3-642-15781-3_17

ABSTRACT Text search engines return a set of k documents ranked by similarity to a query. Typically, documents and queries are drawn from natural language text, which can
readily be partitioned into words, allowing optimizations of data structures and algorithms for ranking. However, in many
new search domains (DNA, multimedia, OCR texts, Far East languages) there is often no obvious definition of words and traditional
indexing approaches are not so easily adapted, or break down entirely. We present two new algorithms for ranking documents
against a query without making any assumptions on the structure of the underlying text. We build on existing theoretical techniques,
which we have implemented and compared empirically with new approaches introduced in this paper. Our best approach is significantly
faster than existing methods in RAM, and is even three times faster than a state-of-the-art inverted file implementation for
English text when word queries are issued.


Full-text (2 Sources)

Available from
May 29, 2014