Similarity-based Queries for XML Databases Using ELIXIR

Several languages for querying and transforming XML, including XML-QL, Quilt, and XQL, have been proposed. However, these languages do not support ranked queries based on textual similarity, in the spirit of traditional IR. Several extensions to these XML query languages to support keyword search have been made, but the resulting languages cannot express IR-style queries such as "find books and CDs with similar titles." In some of these languages keywords are used merely as boolean filters without support for true ranked retrieval; others permit similarity calculations only between a data value and a constant, and thus cannot express the above query. WHIRL avoids both problems, but assumes relational data. We propose ELIXIR, an expressive and efficient language for XML information retrieval that extends XML-QL with a textual similarity operator that can be used for similarity joins, so ELIXIR is sufficiently expressive to handle the sample query above. ELIXIR thus qualifies as a general-purpose XML IR query language. Our central contribution is an efficient algorithm for answering ELIXIR queries that rewrites the original ELIXIR query into a series of XML-QL queries to generate intermediate relational data, and uses WHIRL to efficiently evaluate the similarity operators on this intermediate data, yielding an XML document with nodes ranked by similarity. Our experiments demonstrate that our prototype scales well with the size of the query and the XML data.