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Register a new userTrQuery: An Embedding-based Framework for Recommanding SPARQL Queries
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Added by
Xiaowang Zhang
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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In this paper, we present an embedding-based framework (TrQuery) for recommending solutions of a SPARQL query, including approximate solutions when exact querying solutions are not available due to incompleteness or inconsistencies of real-world RDF data. Within this framework, embedding is applied to score solutions together with edit distance so that we could obtain more fine-grained recommendations than those recommendations via edit distance. For instance, graphs of two querying solutions with a similar structure can be distinguished in our proposed framework while the edit distance depending on structural difference becomes unable. To this end, we propose a novel score model built on vector space generated in embedding system to compute the similarity between an approximate subgraph matching and a whole graph matching. Finally, we evaluate our approach on large RDF datasets DBpedia and YAGO, and experimental results show that TrQuery exhibits an excellent behavior in terms of both effectiveness and efficiency.
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In this paper, we present a MapReduce-based framework for evaluating SPARQL queries on GPU (named MapSQ) to large-scale RDF datesets efficiently by applying both high performance. Firstly, we develop a MapReduce-based Join algorithm to handle SPARQL queries in a parallel way. Secondly, we present a coprocessing strategy to manage the process of evaluating queries where CPU is used to assigns subqueries and GPU is used to compute the join of subqueries. Finally, we implement our proposed framework and evaluate our proposal by comparing with two popular and latest SPARQL query engines gStore and gStoreD on the LUBM benchmark. The experiments demonstrate that our proposal MapSQ is highly efficient and effective (up to 50% speedup).
In the real world datasets (e.g.,DBpedia query log), queries built on well-designed patterns containing only AND and OPT operators (for short, WDAO-patterns) account for a large proportion among all SPARQL queries. In this paper, we present a plugin-based framework for all SELECT queries built on WDAO-patterns, named PIWD. The framework is based on a parse tree called emph{well-designed AND-OPT tree} (for short, WDAO-tree) whose leaves are basic graph patterns (BGP) and inner nodes are the OPT operators. We prove that for any WDAO-pattern, its parse tree can be equivalently transformed into a WDAO-tree. Based on the proposed framework, we can employ any query engine to evaluate BGP for evaluating queries built on WDAO-patterns in a convenient way. Theoretically, we can reduce the query evaluation of WDAO-patterns to subgraph homomorphism as well as BGP since the query evaluation of BGP is equivalent to subgraph homomorphism. Finally, our preliminary experiments on gStore and RDF-3X show that PIWD can answer all queries built on WDAO-patterns effectively and efficiently.
Query response time often influences user experience in the real world. However, it possibly takes more time to answer a query with its all exact solutions, especially when it contains the OPT operations since the OPT operation is the least conventional operator in SPARQL. So it becomes essential to make a trade-off between the query response time and the accuracy of their solutions. In this paper, based on the depth of the OPT operation occurring in a query, we propose an approach to obtain its all approximate queries with less depth of the OPT operation. This paper mainly discusses those queries with well-designed patterns since the OPT operation in a well-designed pattern is really optional. Firstly, we transform a well-designed pattern in OPT normal form into a well-designed tree, whose inner nodes are labeled by OPT operation and leaf nodes are labeled by patterns containing other operations such as the AND operation and the FILTER operation. Secondly, based on this well-designed tree, we remove optional well-designed subtrees with less depth of the OPT operation and then obtain approximate queries with different depths of the OPT operation. Finally, we evaluate the approximate query efficiency with the degree of approximation.
Finding a good query plan is key to the optimization of query runtime. This holds in particular for cost-based federation engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful for the development of future cost-based federated SPARQL query processing engines.
Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effective intention prediction and query optimization. However, these behaviors have not yet been researched systematically at the SPARQL session level. This paper reveals secrets of session-level user search behaviors by conducting a comprehensive investigation over massive real-world SPARQL query logs. In particular, we thoroughly assess query changes made by users w.r.t. structural and data-driven features of SPARQL queries. To illustrate the potentiality of our findings, we employ an application example of how to use our findings, which might be valuable to devise efficient SPARQL caching, auto-completion, query suggestion, approximation, and relaxation techniques in the future.
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