Retrieving and organizing web pages by “information unit”
Proceedings of the 10th international conference on World Wide Web
Implicit link analysis for small web search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
WebGuard: A Web Filtering Engine Combining Textual, Structural, and Visual Content-Based Analysis
IEEE Transactions on Knowledge and Data Engineering
A Reordering for the PageRank Problem
SIAM Journal on Scientific Computing
Finding and approximating top-k answers in keyword proximity search
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
An expert system approach to improving stability and reliability of web service
Expert Systems with Applications: An International Journal
Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Matching twigs in probabilistic XML
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
BioCrawler: An intelligent crawler for the semantic web
Expert Systems with Applications: An International Journal
An ontological website models-supported search agent for web services
Expert Systems with Applications: An International Journal
A practical extension of web usage mining with intentional browsing data toward usage
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
STAR: Steiner-Tree Approximation in Relationship Graphs
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Running tree automata on probabilistic XML
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Expert Systems with Applications: An International Journal
The RDF-3X engine for scalable management of RDF data
The VLDB Journal — The International Journal on Very Large Data Bases
Expressive languages for path queries over graph-structured data
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Discover hierarchical subgraphs with network-topology based ranking score
Proceedings of the Third C* Conference on Computer Science and Software Engineering
Hi-index | 12.05 |
Keyword queries have long been popular to search engines and to the information retrieval community and have recently gained momentum for its usage in the expert systems community. The conventional semantics for processing a user query is to find a set of top-k web pages such that each page contains all user keywords. Recently, this semantics has been extended to find a set of cohesively interconnected pages, each of which contains one of the query keywords scattered across these pages. The keyword query having the extended semantics (i.e., more than a list of keywords hyperlinked with each other) is referred to the graph query. In case of the graph query, all the query keywords may not be present on a single Web page. Thus, a set of Web pages with the corresponding hyperlinks need to be presented as the search result. The existing search systems reveal serious performance problem due to their failure to integrate information from multiple connected resources so that an efficient algorithm for keyword query over graph-structured data is proposed. It integrates information from multiple connected nodes of the graph and generates result trees with the occurrence of all the query keywords. We also investigate a ranking measure called graph ranking score (GRS) to evaluate the relevant graph results so that the score can generate a scalar value for keywords as well as for the topology.