Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Many efforts have been deployed by the IR community to extend free-text
 query processing toward semi-structured XML search. Most methods rely on the
 concept of Lowest Comment Ancestor (LCA) between two or multiple structural
 nodes to identify the most specific XML elements containing query keywords
 posted by the user. Yet, few of the existing approaches consider XML 
 semantics, and the methods that process semantics generally rely on
 computationally expensive word sense disambiguation (WSD) techniques, or
 apply semantic analysis in one stage only: performing query
 relaxation/refinement over the bag of words retrieval model, to reduce
 processing time. In this paper, we describe a new approach for XML keyword
 search aiming to solve the limitations mentioned above. Our solution first
 transforms the XML document collection (offline) and the keyword query
 (on-the-fly) into meaningful semantic representations using context-based
 and global disambiguation methods, specially designed to allow almost linear
 computation efficiency. We use a semantic-aware inverted index to allow
 semantic-aware search, result selection, and result ranking functionality.
 The semantically augmented XML data tree is processed for structural node
 clustering, based on semantic query concepts (i.e., key-concepts), in order
 to identify and rank candidate answer sub-trees containing related
 occurrences of query key-concepts. Dedicated weighting functions and various
 search algorithms have been developed for that purpose and will be presented
 here. Experimental results highlight the quality and potential of our
 approach.