Using structural information in XML keyword search effectively

Abstract

The popularity of XML has exacerbated the need for an easy-to-use, high precision query interface for XML data. When traditional document-oriented keyword search techniques do not suffice, natural language interfaces and keyword search techniques that take advantage of XML structure make it very easy for ordinary users to query XML databases. Unfortunately, current approaches to processing these queries rely heavily on heuristics that are intuitively appealing but ultimately ad hoc. These approaches often retrieve false positive answers, overlook correct answers, and cannot rank answers appropriately. To address these problems for data-centric XML, we propose coherency ranking (CR), a domain- and database design-independent ranking method for XML keyword queries that is based on an extension of the concepts of data dependencies and mutual information. With coherency ranking, the results of a keyword query are invariant under a class of equivalency-preserving schema reorganizations. We analyze the way in which previous approaches to XML keyword search approximate coherency ranking, and present efficient algorithms to process queries and rank their answers using coherency ranking. Our empirical evaluation with two real-world XML data sets shows that coherency ranking has better precision and recall and provides better ranking than all previous approaches.

An Algorithm for Finding top-K Valid XPath Queries

“…There has been a number of studies on XML keyword search (e.g., Refs. [12], [20], [21]), which are especially suitable for users that are not familiar with XML query languages. Several XML editors (e.g., XMLSpy [1]) support auto-complete for XPath query editing, but they do not support listing K correct XPath queries.…”

Gestural query specification

“…However, most existing systems map interaction APIs to isolated database queries [25,47] without any consideration of the query paradigm, query algebra, data or schema of the database [52,36,5,3]. This ad-hoc mapping of gestures, while appealing, does not consider the overall usability of the database querying or its overall effectiveness.…”

Top-k-size keyword search on tree structured data

“…In contrast to keyword search on flat text documents, keyword search on XML (or other tree-structured) data returns not whole documents but appropriately selected fragments of XML trees that contain matches to all the keywords [28,14]. A large number of publications elaborate on the form [14,24,4,5,8,26,23] and the meaningful instances of these result fragments [14,10,22,31,15,29,18,32,25,26,30,17] in the input XML tree. Usually, the query results are the minimum connecting trees that contain one instance of every keyword in the query.…”

“…Approaches that select and return as answer to keyword queries a subset of the LCAs in the XML tree are called filtering because they filter out LCAs that are considered irrelevant [10,22,31,18,32]. Although, filtering approaches are intuitively reasonable, they are sufficiently ad hoc and they are frequently violated in practice resulting in low precision and/or recall [30].…”

“…Ranking the LCAs greatly improves the usability of the system. Most ranking approaches are based on strategies employed for flat text documents (e.g., tf nidf or PageRank) adapted to the hierarchical nature of the XML trees [14,10,30,2]. Recognizing the fact that users are usually interested in a small number of query results, some papers recently develop top-k algorithms for keyword search over XML data [19,8,21].…”