PatentRank: an ontology-based approach to patent search
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Optimal top-k generation of attribute combinations based on ranked lists
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
KESOSD: keyword search over structured data
KEYS '12 Proceedings of the Third International Workshop on Keyword Search on Structured Data
Efficient keyword search on large tree structured datasets
KEYS '12 Proceedings of the Third International Workshop on Keyword Search on Structured Data
MESSIAH: missing element-conscious SLCA nodes search in XML data
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Journal of Information Science
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Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. The difference between text database and XML database results in three new challenges: 1) Identify the user search intention, i.e., identify the XML node types that user wants to search for and search via. 2) Resolve keyword ambiguity problems: a keyword can appear as both a tag name and a text value of some node; a keyword can appear as the text values of different XML node types and carry different meanings; a keyword can appear as the tag name of different XML node types with different meanings. 3) As the search results are subtrees of the XML document, new scoring function is needed to estimate its relevance to a given query. However, existing methods cannot resolve these challenges, thus return low result quality in term of query relevance. In this paper, we propose an IR-style approach which basically utilizes the statistics of underlying XML data to address these challenges. We first propose specific guidelines that a search engine should meet in both search intention identification and relevance oriented ranking for search results. Then, based on these guidelines, we design novel formulae to identify the search for nodes and search via nodes of a query, and present a novel XML TF*IDF ranking strategy to rank the individual matches of all possible search intentions. To complement our result ranking framework, we also take the popularity into consideration for the results that have comparable relevance scores. Lastly, extensive experiments have been conducted to show the effectiveness of our approach.