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27
Retrieving meaningful relaxed tightest fragments for xml keyword search
- In EDBT
, 2009
"... Adapting keyword search to XML data has been attractive recently, generalized as XML keyword search (XKS). One of its key tasks is to return the meaningful fragments as the result. [1] is the latest work following this trend, and it focuses on returning the fragments rooted at SLCA (Smallest LCA – L ..."
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Cited by 16 (0 self)
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Adapting keyword search to XML data has been attractive recently, generalized as XML keyword search (XKS). One of its key tasks is to return the meaningful fragments as the result. [1] is the latest work following this trend, and it focuses on returning the fragments rooted at SLCA (Smallest LCA – Lowest Common Ancestor) nodes. To guarantee that the fragments only contain interesting nodes, [1] proposes a contributor-based filtering mechanism in its MaxMatch algorithm. However, the filtering mechanism is not sufficient. It will commit the false positive problem (discarding interesting nodes) and the redundancy problem (keeping uninteresting nodes). In this paper, our interest is to propose a framework of retrieving meaningful fragments rooted at not only the SLCA nodes, but all LCA nodes. We begin by introducing the concept of Relaxed Tightest Fragment (RTF) as the basic result type. Then we propose a new filtering mechanism to overcome those two problems in Max-Match. Its kernel is the concept of valid contributor, which helps to distinguish the interesting children of a node. The new filtering mechanism is then to prune the nodes in a RTF which are not valid contributors to their parents. Based on the valid contributor concept, our ValidRTF algorithm not only overcomes those two problems in MaxMatch, but also satisfies the axiomatic properties deduced in [1] that an XKS technique should satisfy. We compare ValidRTF with MaxMatch on real and synthetic XML data. The result verifies our claims, and shows the effectiveness of our validcontributor-based filtering mechanism. 1.
Supporting Top-K Keyword Search in XML Databases
"... Abstract — Keyword search is considered to be an effective information discovery method for both structured and semistructured data. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA). However, naive LCA-based semantics leads to exponential computation and ..."
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Abstract — Keyword search is considered to be an effective information discovery method for both structured and semistructured data. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA). However, naive LCA-based semantics leads to exponential computation and result size. In the literature, LCA-based semantic variants (e.g., ELCA and SLCA) were proposed, which define a subset of all the LCAs as the results. While most existing work focuses on algorithmic efficiency, top-K processing for XML keyword search is an important issue that has received very little attention. Existing algorithms focusing on efficiency are designed to optimize the semantic pruning and are incapable of supporting top-K processing. On the other hand, straightforward applications of top-K techniques from other areas (e.g., relational databases) generate LCAs that may not be the results and unnecessarily expand efforts in the semantic pruning. In this paper, we propose a series of join-based algorithms that combine the semantic pruning and the top-K processing to support top-K keyword search in XML databases. The algorithms essentially reduce the keyword query evaluation to relational joins, and incorporate the idea of the top-K join from relational databases. Extensive experimental evaluations show the performance advantages of our algorithms. I.
Suggestion of promising result types for xml keyword search.
- In EDBT,
, 2010
"... ABSTRACT Although keyword query enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages or XML data schemas, the ambiguity of keyword query may result in generating a great number of results that may be classified into different typ ..."
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Cited by 8 (3 self)
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ABSTRACT Although keyword query enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages or XML data schemas, the ambiguity of keyword query may result in generating a great number of results that may be classified into different types. For users, each result type implies a possible search intention. To improve the performance of keyword query, it is desirable to efficiently work out the most relevant result type from the data to be retrieved. Several recent research works have focused on this interesting problem by using data schema information or pure IR-style statical information. However, this problem is still open due to some requirements. (1) The data to be retrieved may not contain schema information; (2) Relevant result types should be efficiently computed before keyword query evaluation; (3) The correlation between a result type and a keyword query should be measured by analyzing the distribution of relevant values and structures within the data. As we know, none of existing work satisfies the above three requirements together. To address the problem, we propose an estimation-based approach to compute the promising result types for a keyword query, which can help a user quickly narrow down to her specific information need. To speed up the computation, we designed new algorithms based on the indexes to be built. Finally, we present a set of experimental results that evaluate the proposed algorithms and show the potential of this work.
Top-k Keyword Search over Probabilistic XML Data
"... far more expensive to answer a keyword query over probabilistic XML data due to the consideration of possible world semantics. In this paper, we firstly define the new problem of studying top-k keyword search over probabilistic XML data, which is to retrieve k SLCA results with the k highest probabi ..."
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Cited by 8 (2 self)
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far more expensive to answer a keyword query over probabilistic XML data due to the consideration of possible world semantics. In this paper, we firstly define the new problem of studying top-k keyword search over probabilistic XML data, which is to retrieve k SLCA results with the k highest probabilities of existence. And then we propose two efficient algorithms. The first algorithm PrStack can find k SLCA results with the k highest probabilities by scanning the relevant keyword nodes only once. To further improve the efficiency, we propose a second algorithm EagerTopK based on a set of pruning properties which can quickly prune unsatisfied SLCA candidates. Finally, we implement the two algorithms and compare their performance with analysis of extensive experimental results. I.
Fast SLCA and ELCA Computation for XML Keyword Queries based on Set Intersection
"... Abstract — In this paper, we focus on efficient keyword query processing for XML data based on SLCA and ELCA semantics. We propose for each keyword a novel form of inverted list, which includes IDs of nodes that directly or indirectly contain the keyword. We propose a family of efficient algorithms ..."
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Cited by 7 (3 self)
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Abstract — In this paper, we focus on efficient keyword query processing for XML data based on SLCA and ELCA semantics. We propose for each keyword a novel form of inverted list, which includes IDs of nodes that directly or indirectly contain the keyword. We propose a family of efficient algorithms that are based on the set intersection operation for both semantics. We show that the problem of SLCA/ELCA computation becomes finding a set of nodes that appear in all involved inverted lists and satisfy certain conditions. We also propose several optimization techniques to further improve the query processing performance. We have conducted extensive experiments with many alternative methods. The results demonstrate that our proposed methods outperform existing ones by up to two orders of magnitude in many cases. I.
A survey of algorithms for keyword search on graph data. Managing and Mining Graph Data
, 2010
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Providing Built-in Keyword Search Capabilities in RDBMS
, 2009
"... Abstract Acommonapproachtoperformingkeywordsearch overrelational databases is to find the minimum Steiner trees in database graphs transformed from relational data. These methods, however, are rather expensive as the minimum Steiner tree problem is known to be NP-hard. Further, these methods are ind ..."
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Cited by 5 (3 self)
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Abstract Acommonapproachtoperformingkeywordsearch overrelational databases is to find the minimum Steiner trees in database graphs transformed from relational data. These methods, however, are rather expensive as the minimum Steiner tree problem is known to be NP-hard. Further, these methods are independent of the underlying relational database management system (RDBMS), thus cannot benefit from the capabilities of the RDBMS. As an alternative, in this paper we propose a new concept called Compact Steiner Tree (CSTree), which can be used to approximate the Steiner tree problem for answering top-k keyword queries efficiently. We propose a novel structureaware index, together with an effective ranking mechanism for fast, progressive and accurate retrieval of top-k highest ranked CSTrees. The proposed techniques can be implemented using a standard relational RDBMS to benefit from its indexing and query processing capability. We have implemented our techniques in MYSQL, which can provide built-in keyword-search capabilities using SQL. The experimental results show a significant improvement in both search efficiency and result quality comparing to existing state-of-the-art approaches. 1
Improving the performance of identifying contributors for XML keyword search
- SIGMOD Rec
"... Keyword search is a friendly mechanism for users to identify desired information in XML databases, and LCA is a popular concept for locating the meaningful subtrees ..."
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Keyword search is a friendly mechanism for users to identify desired information in XML databases, and LCA is a popular concept for locating the meaningful subtrees
Effective Ranking of XML Keyword Search Results (Extended Version)
"... 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 eas ..."
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Cited by 2 (2 self)
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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, 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 schema reorganization. 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. Coherency ranking can also be used
Lca-based selection for xml document collections
- in WWW, 2010
"... ABSTRACT In this paper, we address the problem of database selection for XML document collections, that is, given a set of collections and a user query, how to rank the collections based on their goodness to the query. Goodness is determined by the relevance of the documents in the collection to th ..."
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ABSTRACT In this paper, we address the problem of database selection for XML document collections, that is, given a set of collections and a user query, how to rank the collections based on their goodness to the query. Goodness is determined by the relevance of the documents in the collection to the query. We consider keyword queries and support Lowest Common Ancestor (LCA) semantics for defining query results, where the relevance of each document to a query is determined by properties of the LCA of those nodes in the XML document that contain the query keywords. To avoid evaluating queries against each document in a collection, we propose maintaining in a preprocessing phase, information about the LCAs of all pairs of keywords in a document and use it to approximate the properties of the LCA-based results of a query. To improve storage and processing efficiency, we use appropriate summaries of the LCA information based on Bloom filters. We address both a boolean and a weighted version of the database selection problem. Our experimental results show that our approach incurs low errors in the estimation of the goodness of a collection and provides rankings that are very close to the actual ones.
