text: In contrast to XML query languages as e.g. XPath which require knowledge on the query language as well as on the document structure, keyword search is open to anybody. As the size of XML sources grows rapidly, the need for efficient search indices on XML data that support keyword search increases. In this paper, we present an approach of XML keyword search which is based on the DAG of the XML data, where repeated substructures are considered only once, and therefore, have to be searched only once. As our performance evaluation shows, this DAG-based extension of the set intersection search algorithm[1], [2], can lead to search times that are on large documents more than twice as fast as the search times of the XML-based approach. Additionally, we utilize a smaller index, i.e., we consume less main memory to compute the results.
  @cite_6: Keyword search is considered to be an effective information discovery method for both structured and semi-structured data. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA). However, naive LCA-based semantics leads to exponential computation and result size. In the literature, LCA-based semantic variants (e.g., ELCA and SLCA) were proposed, which define a subset of all the LCAs as the results. While most existing work focuses on algorithmic efficiency, top-K processing for XML keyword search is an important issue that has received very little attention. Existing algorithms focusing on efficiency are designed to optimize the semantic pruning and are incapable of supporting top-K processing. On the other hand, straightforward applications of top-K techniques from other areas (e.g., relational databases) generate LCAs that may not be the results and unnecessarily expand efforts in the semantic pruning. In this paper, we propose a series of join-based algorithms that combine the semantic pruning and the top-K processing to support top-K keyword search in XML databases. The algorithms essentially reduce the keyword query evaluation to relational joins, and incorporate the idea of the top-K join from relational databases. Extensive experimental evaluations show the performance advantages of our algorithms.
summary: JDeweyJoin @cite_6 returns the top-k most relevant results. They compute the results bottom-up by computing a kind of join on the list of DeweyIDs of the nodes in the inverted element list. Whenever they find a prefix that is contained in all relevant element lists, the node with this prefix as ID is a result candidate. In addition, they use a weight function to sort the list entries in such a way, that they can stop the computation after k results, returning the top-k most relevant results.