BinRank: Scaling Dynamic Authority-Based Search Using Materialized SubGraphs

Hwang, Heasoo; Balmin, Andrey; Reinwald, Berthold; Nijkamp, Erik

doi:10.1109/icde.2009.94

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Using materialized views, BinRank approximates ObjectRank results achieving a faster and higher quality search results. Furthermore, Hwang et al [57] used BinRank to present a solution for a dynamic authority-based ranking problem by computing a number of materialized subgraphs. The full English Wikipedia data set exported in October 2007 was employed to test and support the performance claims of this solution.…”

Section: Information Retrievalmentioning

confidence: 99%

Excavating the mother lode of human-generated text: A systematic review of research that uses the wikipedia corpus

Mehdi

Okoli

Mesgari

et al. 2017

Information Processing & Management

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Although primarily an encyclopedia, Wikipedia's expansive content provides a knowledge base that has been continuously exploited by researchers in a wide variety of domains. This article systematically reviews the scholarly studies that have used Wikipedia as a data source, and investigates the means by which Wikipedia has been employed in three main computer science research areas: information retrieval, natural language processing, and ontology building. We report and discuss the research trends of the identified and examined studies. We further identify and classify a list of tools that can be used to extract data from Wikipedia, and compile a list of currently available data sets extracted from Wikipedia.

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Section: Information Retrievalmentioning

confidence: 99%

Excavating the mother lode of human-generated text: A systematic review of research that uses the wikipedia corpus

Mehdi

Okoli

Mesgari

et al. 2017

Information Processing & Management

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“…Secondly, ObjectRank changes the role of the personalization vector from ranking to search. The topics are replaced with query entities, and the schema is weighted to form an ER graph in ObjectRank and its variations [28–31]. …”

Section: Related Workmentioning

confidence: 99%

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships

Zong

Lee

Ahn

et al. 2017

Computers in Biology and Medicine

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Objective The keyword-based entity search restricts search space based on the preference of search. When given keywords and preferences are not related to the same biomedical topic, existing biomedical Linked Data search engines fail to deliver satisfactory results. This research aims to tackle this issue by supporting an inter-topic search—improving search with inputs, keywords and preferences, under different topics. Methods This study developed an effective algorithm in which the relations between biomedical entities were used in tandem with a keyword-based entity search, Siren. The algorithm, PERank, which is an adaptation of Personalized PageRank (PPR), uses a pair of input: (1) search preferences, and (2) entities from a keyword-based entity search with a keyword query, to formalize the search results on-the-fly based on the index of the precomputed Individual Personalized PageRank Vectors (IPPVs). Results Our experiments were performed over ten linked life datasets for two query sets, one with keyword-preference topic correspondence (intra-topic search), and the other without (inter-topic search). The experiments showed that the proposed method achieved better search results, for example a 14% increase in precision for the inter-topic search than the baseline keyword-based search engine. Conclusion The proposed method improved the keyword-based biomedical entity search by supporting the inter-topic search without affecting the intra-topic search based on the relations between different entities.

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“…Personalized PageRank [15,18,7,19] is a personalization model specifically studied in web search. Instead of using the uniform distribution for all nodes at the initial state, personalized PageRank uses a set of query or user-specific nodes as the random walk starting points.…”

Section: Related Workmentioning

confidence: 99%

“…The main challenge is how to compute personalized scores efficiently, as online computations usually involve expensive fixpoint iterations over a very large graph. Among the proposed solutions, the algorithms in [18,19] share the same spirit of the materialized view technique: some small subgraphs are precomputed in advance. Online computations use the materialized subgraphs to improve efficiency.…”

Section: Related Workmentioning

confidence: 99%

Context-sensitive ranking for document retrieval

Chen

Papakonstantinou

2011

Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data

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We study the problem of context-sensitive ranking for document retrieval, where a context is defined as a sub-collection of documents, and is specified by queries provided by domain-interested users. The motivation of context-sensitive search is that the ranking of the same keyword query generally depends on the context. The reason is that the underlying keyword statistics differ significantly from one context to another. The query evaluation challenge is the computation of keyword statistics at runtime, which involves expensive online aggregations. We appropriately leverage and extend materialized view research in order to deliver algorithms and data structures that evaluate context-sensitive queries efficiently. Specifically, a number of views are selected and materialized, each corresponding to one or more large contexts. Materialized views are used at query time to compute statistics which are used to compute ranking scores. Experimental results show that the contextsensitive ranking generally improves the ranking quality, while our materialized view-based technique improves the query efficiency.

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BinRank: Scaling Dynamic Authority-Based Search Using Materialized SubGraphs

Cited by 7 publications

References 8 publications

Excavating the mother lode of human-generated text: A systematic review of research that uses the wikipedia corpus

Excavating the mother lode of human-generated text: A systematic review of research that uses the wikipedia corpus

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships

Context-sensitive ranking for document retrieval

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