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A Learning Based Framework for Improving Querying on Web Interfaces of Curated Knowledge Bases

Zhang, Wei Emma, Sheng, Quan Z., Yao, Lina, Taylor, Kerry, Shemshadi, Ali and Qin, Yongrui (2017) A Learning Based Framework for Improving Querying on Web Interfaces of Curated Knowledge Bases. ACM Transactions on Internet Technology (TOIT). ISSN 1533-5399 (In Press)

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Knowledge Bases (KBs) are widely used as one of the fundamental components in Semantic Web applications
as they provide facts and relationships that can be automatically understood by machines. Curated knowledge
bases usually use Resource Description Framework (RDF) as the data representation model. In order to query
the RDF-presented knowledge in curated KBs, Web interfaces are built via SPARQL Endpoints. Currently,
querying SPARQL Endpoints has the problems like network instability and latency, which affect the query
efficiency. To address these issues, we propose a client-side caching framework, SPARQL Endpoint Caching
Framework (SECF), aiming at accelerating the overall querying speed over SPARQL Endpoints. SECF identifies
the potential issued queries by leveraging the querying patterns learned from clients’ historical queries and
prefecthes/caches these queries. In particular, we develop a distance function based on graph edit distance to
measure the similarity of SPARQL queries. We propose a feature modelling method to transform SPARQL
queries to vector representation that are fed into machine learning algorithms. A time-aware smoothing-based
method, Modified Simple Exponential Smoothing (MSES), is developed for cache replacement. Extensive
experiments performed on real world queries showcase the effectiveness of our approach, which outperforms
the state-of-the-art work in terms of the overall querying speed.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Sally Hughes
Date Deposited: 11 Jan 2018 14:44
Last Modified: 11 Jan 2018 14:48


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