Paper Info
Title
Effective top-k computation in retrieving structured documents with term-proximity support
Abstract
Modern web search engines are expected to return top-k results efficiently given a query. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignore some especially important factors in ranking functions, e.g. term proximity (the distance relationship between query terms in a document). The inclusion of term proximity breaks the monotonicity of ranking functions and therefore leads to additional challenges for efficient query processing. This paper studies the performance of some existing top-k computation approaches using term-proximity-enabled ranking functions. Our investigation demonstrates that, when term proximity is incorporated into ranking functions, most existing index structures and top-k strategies become quite inefficient. According to our analysis and experimental results, we propose two index structures and their corresponding index pruning strategies: Structured and Hybrid, which performs much better on the new settings. Moreover, the efficiency of index building and maintenance would not be affected too much with the two approaches.
Year
DOI
Venue
2007
10.1145/1321440.1321547
CIKM
Keywords
Field
DocType
structured document,term-proximity-enabled ranking function,effective top-k computation,index structure,existing top-k computation,ranking function,dynamic index pruning strategy,index building,existing index structure,term-proximity support,efficient top-k computation,corresponding index pruning strategy,term proximity,indexation,web search engine,document structure
Monotonic function,Data mining,Search engine,Information retrieval,Ranking,Computer science,Document Structure Description,Ranking (information retrieval),Computation
Conference
Citations 
PageRank 
References 
14
0.61
23
Authors
4
Name
Order
Citations
PageRank
Mingjie Zhu1894.32
Shuming Shi262058.27
Mingjing Li33076192.39
Ji-Rong Wen44431265.98