Extended Characteristic Sets: Graph Indexing for SPARQL Query Optimization

Meimaris, Marios; Papastefanatos, George; Mamoulis, Nikos; Anagnostopoulos, Ioannis

doi:10.1109/icde.2017.106

Cited by 32 publications

(37 citation statements)

References 15 publications

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“…Families provide structure-based means of partitioning an RDF graph used for (i) join and cardinality estimation [21,37] for SPARQL optimization, (ii) RDF compression [31], and (iii) building indexes 2 to speed up SPARQL queries [35]. To the best of our knowledge, this paper is the first work using families in shipping strategies.…”

Section: Preliminariesmentioning

confidence: 99%

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SMART-KG: Hybrid Shipping for SPARQL Querying on the Web

Azzam

Fernández

Acosta

et al. 2020

Proceedings of the Web Conference 2020

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While Linked Data (LD) provides standards for publishing (RDF) and (SPARQL) querying Knowledge Graphs (KGs) on the Web, serving, accessing and processing such open, decentralized KGs is often practically impossible, as query timeouts on publicly available SPARQL endpoints show. Alternative solutions such as Triple Pattern Fragments (TPF) attempt to tackle the problem of availability by pushing query processing workload to the client side, but suffer from unnecessary transfer of irrelevant data on complex queries with large intermediate results. In this paper we present smart-KG, a novel approach to share the load between servers and clients, while significantly reducing data transfer volume, by combining TPF with shipping compressed KG partitions. Our evaluations show that smart-KG outperforms state-of-the-art client-side solutions and increases server-side availability towards more cost-effective and balanced hosting of open and decentralized KGs.

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Section: Preliminariesmentioning

confidence: 99%

“…i.e, G = G 1 ∪ G 2 ∪ • • • ∪ G m , where ∀i, j G i ∩ G j = ∅ 2Meimaris et al[35] extended the notion of characteristic sets also to object nodes…”

mentioning

confidence: 99%

SMART-KG: Hybrid Shipping for SPARQL Querying on the Web

Azzam

Fernández

Acosta

et al. 2020

Proceedings of the Web Conference 2020

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“…Typically, any set of joined triple patterns in a query can be divided into connected star-shaped subqueries. Subqueries in combination with the predicate that links them, define a characteristic pair (CP) [8,11]. Statistics about such CPs can then be used to estimate the selectivity of two star-shaped subqueries.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by the latest advances in statistics for centralized triple stores [8,11,12], Odyssey uses statistics about individual datasets to derive detailed statistics for optimizing federated queries. In the following, we first describe the foundations of our statistics on individual datasets (Section 3.1) and then propose a novel method for computing such statistics in a federated environment based on entity descriptions (Section 3.2).…”

Section: The Odyssey Approachmentioning

confidence: 99%

The Odyssey Approach for Optimizing Federated SPARQL Queries

Montoya

Skaf-Molli

Hose

2017

Lecture Notes in Computer Science

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Abstract. Answering queries over a federation of SPARQL endpoints requires combining data from more than one data source. Optimizing queries in such scenarios is particularly challenging not only because of (i) the large variety of possible query execution plans that correctly answer the query but also because (ii) there is only limited access to statistics about schema and instance data of remote sources. To overcome these challenges, most federated query engines rely on heuristics to reduce the space of possible query execution plans or on dynamic programming strategies to produce optimal plans. Nevertheless, these plans may still exhibit a high number of intermediate results or high execution times because of heuristics and inaccurate cost estimations. In this paper, we present Odyssey, an approach that uses statistics that allow for a more accurate cost estimation for federated queries and therefore enables Odyssey to produce better query execution plans. Our experimental results show that Odyssey produces query execution plans that are better in terms of data transfer and execution time than state-of-theart optimizers. Our experiments using the FedBench benchmark show execution time gains of at least 25 times on average.

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“…Similarly, DBpedia is based on an ontology, represented in the namespace dbo: (http://dbpedia.org/ontology/), that defines various classes that are used to type available entities (resources). DBpedia refers to and is referenced by several datasets on the LOD and it is used for tasks as diverse as semantic annotation [12], knowledge extraction [13], information retrieval [14], querying [15,16] and question answering [17].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection

2018

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This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution.

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Extended Characteristic Sets: Graph Indexing for SPARQL Query Optimization

Cited by 32 publications

References 15 publications

SMART-KG: Hybrid Shipping for SPARQL Querying on the Web

SMART-KG: Hybrid Shipping for SPARQL Querying on the Web

The Odyssey Approach for Optimizing Federated SPARQL Queries

A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection

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