BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs
Authors | Heasoo Hwang Andrey Balmin Berthold Reinwald Erik Nijkamp |
|---|---|
Publication date | 2010 |
ISSN | 10414347 |
DOI | 10.1109/TKDE.2010.85 |
Links | Original |
BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs - scientific work about Wikipedia quality published in 2010, written by Heasoo Hwang, Andrey Balmin, Berthold Reinwald and Erik Nijkamp.
Overview
Dynamic authority-based keyword search algorithms, such as ObjectRank and personalized PageRank, leverage semantic link information to provide high quality, high recall search in databases and on the Web. Conceptually, these algorithms require a query-time PageRank-style iterative computation over the full graph. This computation is too expensive for large graphs, and not feasible at query time. Alternatively, building an index of pre-computed results for some or all keywords involves very expensive preprocessing. Authors introduce BinRank, a system that approximates ObjectRank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. Authors materialize a number of relatively small subsets of the data graph in such a way that any keyword query can be answered by running ObjectRank on only one of the sub-graphs. BinRank generates the sub-graphs by partitioning all the terms in the corpus based on their co-occurrence, executing ObjectRank for each partition using the terms to generate a set of random walk starting points, and keeping only those objects that receive nonnegligible scores. The intuition is that a sub-graph that contains all objects and links relevant to a set of related terms should have all the information needed to rank objects with respect to one of these terms. Authors demonstrate that BinRank can achieve sub-second query execution time on the English Wikipedia dataset, while producing high quality search results that closely approximate the results of ObjectRank on the original graph. The Wikipedia link graph contains about 108 edges, which is at least two orders of magnitude larger than what prior state of the art dynamic authority-based search systems have been able to demonstrate. Their experimental evaluation investigates the trade-off between query execution time, quality of the results, and storage requirements of BinRank.
Embed
Wikipedia Quality
Hwang, Heasoo; Balmin, Andrey; Reinwald, Berthold; Nijkamp, Erik. (2010). "[[BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs]]". IEEE Transactions on Knowledge and Data Engineering Volume 22, Issue 8, 2010, Article number 5467077, pp. 1176-1190. ISSN: 10414347. DOI: 10.1109/TKDE.2010.85.
English Wikipedia
{{cite journal |last1=Hwang |first1=Heasoo |last2=Balmin |first2=Andrey |last3=Reinwald |first3=Berthold |last4=Nijkamp |first4=Erik |title=BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs |date=2010 |issn=10414347 |doi=10.1109/TKDE.2010.85 |url=https://wikipediaquality.com/wiki/BinRank:_Scaling_Dynamic_Authority-Based_Search_Using_Materialized_Subgraphs |journal=IEEE Transactions on Knowledge and Data Engineering Volume 22, Issue 8, 2010, Article number 5467077, pp. 1176-1190}}
HTML
Hwang, Heasoo; Balmin, Andrey; Reinwald, Berthold; Nijkamp, Erik. (2010). "<a href="https://wikipediaquality.com/wiki/BinRank:_Scaling_Dynamic_Authority-Based_Search_Using_Materialized_Subgraphs">BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs</a>". IEEE Transactions on Knowledge and Data Engineering Volume 22, Issue 8, 2010, Article number 5467077, pp. 1176-1190. ISSN: 10414347. DOI: 10.1109/TKDE.2010.85.
