Approximating query answering on RDF databases

Huang, Hai; Liu, Chengfei; Zhou, Xiaofang

doi:10.1007/s11280-011-0131-7

Cited by 39 publications

(43 citation statements)

References 26 publications

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“…These are orthogonal to our work, as reliable error estimates from an underlying AQP engine will also benefit DBL. There is also AQP techniques developed for specific domain, e.g., sequential data [11,64], probabilistic data [30,59], and RDF data [37,76], and searching in high-dimensional space [62]. However, our focus in this paper is on general (SQL-based) AQP engines.…”

Section: Related Workmentioning

confidence: 99%

Database Learning

Park

Tajik

Cafarella

et al. 2017

Proceedings of the 2017 ACM International Conference on Management of Data

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In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries.We call this novel idea-learning from past query answersDatabase Learning. We exploit the principle of maximum entropy to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that Verdict supports 73.7% of these queries, speeding them up by up to 23.0× for the same accuracy level compared to existing AQP systems.

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Section: Related Workmentioning

confidence: 99%

Database Learning

Park

Tajik

Cafarella

et al. 2017

Proceedings of the 2017 ACM International Conference on Management of Data

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“…7), we show the difference of the procession between our approach of correction and relaxation, and the other approaches that use the only the relaxation [23] [24] [32]. The value of the difference is in the interval [-1, 1].…”

Section: Evolution Resultsmentioning

confidence: 99%

On Improving SPARQL Information Retrieval System for Semantic Internet of Things Applications

Jalal¹,

Ouni²,

Kamoun³

et al. 2018

IJCA

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Due to the huge volume of information and knowledge derived from IoT devices, it will be hard to explore all knowledge coming from these devices. The semantic ontology-based descriptions with the semantic web are one of the most interesting ways to extract the required knowledge. However, the information retrieval typically made by the SPARQL querying language stills hard to write correct queries from what the users need as knowledge to extract. It needs a deep knowledge of the semantic information system structure.In this paper, we have developed a new correction and relaxation approach based on structural semantic similarity measure to overcome the semantic errors in SPARQL queries. This approach is applied to semantic information systems using OWL and RDF ontologies which are related to IoT applications. To achieve the efficiency of our proposal, we have developed a SPARQL querying tools. According to the queries made on IoT applications, our approach performs best results regarding the precision of the answer to these queries.

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“…Graph queries via structure similarity. Structure similarity based graph pattern match is one commonly used technology to support query over knowledge graph . Zheng et al proposed a instance‐driven mining algorithm to detect diverse structure patterns with equivalent semantic meanings of priori knowledge .…”

Section: Related Workmentioning

confidence: 99%

Semantic locality‐based approximate knowledge graph query

Wang

Yan

et al. 2019

Concurrency and Computation

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Summary RDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired results efficiently on RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the “schema‐free” feature of RDF KG. In this paper, we study the problem of acquiring semantic approximate results for a simple SPARQL query instead of the complex expression. The basic idea behind is to use the real knowledge semantics to extend the query intention of given simplified SPARQL query, and then a semantic based greedy search over is designed to return top‐k similar results. To obtain the knowledge semantics efficiently, we define type similarity to reorganize the original KG as a corpus with semantic locality and then a context aware text embedding model is adopted to achieve the semantic vectors of existing knowledge. Afterwards, an approximate query method over RDF KG is designed to obtain top‐k similar results based on the knowledge semantics above. Extensive experiments over DBpedia dataset and QALD‐4 benchmark confirm our solution's superiority on both effectiveness and efficiency.

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Approximating query answering on RDF databases

Cited by 39 publications

References 26 publications

Database Learning

Database Learning

On Improving SPARQL Information Retrieval System for Semantic Internet of Things Applications

Semantic locality‐based approximate knowledge graph query

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