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.
Within the first category, XSEarch @cite_15 presents a query semantics that returns only those XML fragments, the result nodes of which are , i.e., intuitively belong to the same entity. In order to check this, they examine whether a pair of result nodes has two different ancestor nodes that have the same label (e.g., two nodes with label author'', s.th. the first keyword belongs to author1 and the second one to author2).
@cite_15
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XSEarch, a semantic search engine for XML, is presented. XSEarch has a simple query language, suitable for a naive user. It returns semantically related document fragments that satisfy the user's query. Query answers are ranked using extended information-retrieval techniques and are generated in an order similar to the ranking. Advanced indexing techniques were developed to facilitate efficient implementation of XSEarch. The performance of the different techniques as well as the recall and the precision were measured experimentally. These experiments indicate that XSEarch is efficient, scalable and ranks quality results highly. We give a representation for labeled ordered trees that supports labeled queries such as finding the i-th ancestor of a node with a given label. Our representation is succinct, namely the redundancy is small-o of the optimal space for storing the tree. This improves the representation of (ISAAC 2012) 8 which is succinct only when the entropy of the labels is ω ( 1 ) . We present a labeling scheme for rooted trees which allows to compute, from the label of v alone, unique identifiers for the ancestors of v that are at distance at most d from v. For any constant d our labeling scheme produce labels of length log n+O(√log n), and for d ∈ O(√log n) the labels are still of length O(log n). In particular, given the labels of two nodes u and v we can determine from the labels alone whether u is the parent of v or vice versa, whether u and v are siblings, and whether u and v are at distance at most d from each other. The need for such labeling scheme arises in several application areas, including in particular communication networks and search engines for large collections ofWeb XML files. In the latter application XML files are viewed as trees, and typical queries ask for XML files containing a particular set of nodes with specific ancestor, parent, or sibling relationships among them. When users issue a query to a database, they have expectations about the results. If what they search for is unavailable in the database, the system will return an empty result or, worse, erroneous mismatch results. We call this problem the MisMatch problem. In this paper, we solve the MisMatch problem in the context of XML keyword search. Our solution is based on two novel concepts that we introduce: target node type and Distinguishability. Target Node Type represents the type of node a query result intends to match, and Distinguishability is used to measure the importance of the query keywords. Using these concepts, we develop a low-cost post-processing algorithm on the results of query evaluation to detect the MisMatch problem and generate helpful suggestions to users. Our approach has three noteworthy features: (1) for queries with the MisMatch problem, it generates the explanation, suggested queries and their sample results as the output to users, helping users judge whether the MisMatch problem is solved without reading all query results; (2) it is portable as it can work with any lowest common ancestor-based matching semantics (for XML data without ID references) or minimal Steiner tree-based matching semantics (for XML data with ID references) which return tree structures as results. It is orthogonal to the choice of result retrieval method adopted; (3) it is lightweight in the way that it occupies a very small proportion of the whole query evaluation time. Extensive experiments on three real datasets verify the effectiveness, efficiency and scalability of our approach. A search engine called XClear has been built and is available at http: xclear.comp.nus.edu.sg.