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Type: Journal article
Title: A learning-based framework for improving querying on web interfaces of curated knowledge bases
Author: Zhang, W.
Sheng, Q.
Yao, L.
Taylor, K.
Shemshadi, A.
Qin, Y.
Citation: ACM Transactions on Internet Technology, 2018; 18(3):35-1-35-20
Publisher: Association for Computing Machinery
Issue Date: 2018
ISSN: 1533-5399
Statement of
Wei Emma Zhang, Quan Z. Sheng, Lina Yao, Kerry Taylor, Ali Shemshadi, Yongrui Qin
Abstract: 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. To query the RDF-presented knowledge in curated KBs, Web interfaces are built via SPARQL Endpoints. Currently, querying SPARQL Endpoints has 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.
Keywords: Knowledge base query-answering; query suggestion; caching; SPARQL
Rights: © 2018 ACM Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
RMID: 0030119258
DOI: 10.1145/3155806
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