XRANK: ranked keyword search over XML documents

By Lin Guo, Feng Shao, Chavdar Botev, Jayavel Shanmugasundaram. ACM SIGMOD Conference, 2003

923 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5b91a283f28b1f5a3137737eb751bc12b8d71a19

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

Spark: top-k keyword query in relational databases (2007, background): “Some advanced features, such as schema term awareness and phrase-based ranking, are also proposed [10], [26], [27], [29], [30], [32], [34], [35], [37], [40], [46]–[51].”
ObjectRank: Authority-Based Keyword Search in Databases (2004, methodology): “Recently, the idea of PageRank has been applied to structured databases [16, 21].”
Efficient IR-Style Keyword Search over Relational Databases (2003, background): “Finally, XRANK [9] proposes a ranking function for the XML “result trees”, which combines the scores of the individual nodes of the result tree.”
Efficient keyword search for smallest LCAs in XML databases (2005, methodology): “The experimental evaluation compares the Indexed Loo kup Eager, Scan Eager and Stack (described in [13]) algorithms.”
Principles of dataspace systems (2006, background): “Some work has already been done on ranking answers in the context of keyword queries over relational databases and over XML documents [3, 4, 9, 30, 33], and on finding relevant information sources in large collections of formally described sources [41].”
Bidirectional Expansion For Keyword Search on Graph Databases (2005, methodology): “There are many scenarios where data forms arbitrary graphs, and cannot be meaningfully modeled by tree structures or even DAG structures; the XRank index structure (and other common XML index structures) cannot be used in such scenarios.”
EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data (2008, background): “To answer the keyword query " Shanmugasundaram, Guo,XRANK", we .rst retrieve the r-radius graphs, Gpr 1 and Gpr 2, which contain the three keywords, based on the graph index, and then extract the corresponding r-radius Steiner graphs, i.e., the circled subgraphs as illustrated in Figure 3, by rem”
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data (2009, methodology): “Much work has been carried out on keyword search over tree-structured data (e.g. [6], [7], [8], [9], [10], [11], [12], [13]) as well as graph-structured data (e.g. [2], [14], [1]).”
Object-level ranking: bringing order to Web objects (2005, methodology): “[7] introduce XRANK to rank XML Figure 12: Ranking Distance for Different Diameters”
XSEarch: A Semantic Search Engine for XML (2003, methodology): “A recent related work is the XRANK system [12] for keyword searching in XML documents.”
A survey of top-k query processing techniques in relational database systems (2008, methodology): “The XRank system [Guo et al. 2003] addresses these issues.”
BLINKS: ranked keyword searches on graphs (2007, methodology): “Techniques developed for XML [5, 14, 25], which take advantage of the hierarchical property of trees, no longer apply.”
Schema-Free XQuery (2004, methodology): “Similar approach has also been taken to apply keyword search in XML documents (e.g., XKeyword [17] and XRANK [15]).”
Effective keyword search in relational databases (2006, background): “Another different but related research topic is keyword search in XML databases [6, 8, 13].”