Integrating keyword search into XML query processing
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Flexible queries over semistructured data
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Covering indexes for branching path queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Holistic twig joins: optimal XML pattern matching
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Querying XML Documents Made Easy: Nearest Concept Queries
Proceedings of the 17th International Conference on Data Engineering
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Structural Joins: A Primitive for Efficient XML Query Pattern Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Efficient keyword search for smallest LCAs in XML databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Benefits of path summaries in an XML query optimizer supporting multiple access methods
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Semantic querying of tree-structured data sources using partially specified tree patterns
Proceedings of the 14th ACM international conference on Information and knowledge management
Keyword Proximity Search in XML Trees
IEEE Transactions on Knowledge and Data Engineering
Heuristic containment check of partial tree-pattern queries in the presence of index graphs
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
XSEarch: a semantic search engine for XML
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Retrieving meaningful relaxed tightest fragments for XML keyword search
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Efficient keyword search on large tree structured datasets
KEYS '12 Proceedings of the Third International Workshop on Keyword Search on Structured Data
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XML is by now the de facto standard for exporting and exchanging data on the web. The need for querying XML data sources whose structure is not fully known to the user and the need to integrate multiple data sources with different tree structures have motivated recently the suggestion of keyword-based techniques for querying XML documents. The semantics adopted by these approaches aims at restricting the answers to meaningful ones. However, these approaches suffer from low precision, while recent ones with improved precision suffer from low recall. In this paper, we introduce an original approach for assigning semantics to keyword queries for XML documents. We exploit index graphs (a structural summary of data) to extract tree patterns that return meaningful answers. In contrast to previous approaches that operate locally on the data to compute meaningful answers (usually by computing lowest common ancestors), our approach operates globally on index graphs to detect and exploit meaningful tree patterns. We implemented and experimentally evaluated our approach on DBLP-based data sets with irregularities. Its comparison to previous ones shows that it succeeds in finding all the meaningful answers when the others fail (perfect recall). Further, it outperforms approaches with similar recall in excluding meaningless answers (better precision). Since our approach is based on tree-pattern query evaluation, it can be easily implemented on top of an XQuery engine.