SHACL and ShEx in the Wild: A Community Survey on Validating Shapes Generation and Adoption

Rabbani, Kashif; Lissandrini, Matteo; Hose, Katja

doi:10.1145/3487553.3524253

Cited by 10 publications

(15 citation statements)

References 13 publications

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“…As such, our future work includes looking into the optimization problem for path queries, e.g., by co-locating relevant fragments for common path patterns on the same nodes, similar to workload-aware partitioning techniques [8,39]. Furthermore, a complete analysis of the effects of graph complexity metrics, like density and centrality, on the fragment skew, query performance, and indexing strategy, as well as an analysis of different fragmentation techniques, e.g., based on SHACL/ShEx shapes [59,60], is important future work. We also plan to expand the range of supported queries to include aggregation and analytical queries [25,41], and add support for provenance both for data [11,29,31], so that the system has information about the origin of the data it uses, as well as for queries [35] so that the system can explain how query answers were computed.…”

Section: Discussionmentioning

confidence: 99%

Optimizing SPARQL queries over decentralized knowledge graphs

Aebeloe

Montoya

Hose

2023

Self Cite

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While the Web of Data in principle offers access to a wide range of interlinked data, the architecture of the Semantic Web today relies mostly on the data providers to maintain access to their data through SPARQL endpoints. Several studies, however, have shown that such endpoints often experience downtime, meaning that the data they maintain becomes inaccessible. While decentralized systems based on Peer-to-Peer (P2P) technology have previously shown to increase the availability of knowledge graphs, even when a large proportion of the nodes fail, processing queries in such a setup can be an expensive task since data necessary to answer a single query might be distributed over multiple nodes. In this paper, we therefore propose an approach to optimizing SPARQL queries over decentralized knowledge graphs, called Lothbrok. While there are potentially many aspects to consider when optimizing such queries, we focus on three aspects: cardinality estimation, locality awareness, and data fragmentation. We empirically show that Lothbrok is able to achieve significantly faster query processing performance compared to the state of the art when processing challenging queries as well as when the network is under high load.

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Section: Discussionmentioning

confidence: 99%

Optimizing SPARQL queries over decentralized knowledge graphs

Aebeloe

Montoya

Hose

2023

Self Cite

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“…However, the tool cannot process large KGs, like Wikidata, without specifying a limit to the number of SPARQL query results. The experiments presented in [17] demonstrate that currently available methods cannot handle the scale of large knowledge graphs such as Wikidata: they crash even with KGs with a few millions triples. 5 sheXer [10] is a tool that automatically extracts shapes, serialising them in both ShEx and SHACL, by mining the graph structure and exploring the neighborhood of predefined target nodes.…”

Section: Carriero Et Al / Empirical Ontology Design Patterns and Shap...mentioning

confidence: 99%

“…As shown by [17], all existing approaches that automatically generate shapes include in such shapes a high number of constraints such that it is non-trivial for a human user to assess their correctness and validity. Moreover, in most cases, no constraint is produced for non-literal objects, i.e.…”

Section: Carriero Et Al / Empirical Ontology Design Patterns and Shap...mentioning

confidence: 99%

Empirical ontology design patterns and shapes from Wikidata

Carriero,

Groth,

Presutti

2024

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The ontology underlying the Wikidata knowledge graph (KG) has not been formalized. Instead, its semantics emerges bottom-up from the use of its classes and properties. Flexible guidelines and rules have been defined by the Wikidata project for the use of its ontology, however, it is still often difficult to reuse the ontology’s constructs. Based on the assumption that identifying ontology design patterns from a knowledge graph contributes to making its (possibly) implicit ontology emerge, in this paper we present a method for extracting what we term empirical ontology design patterns (EODPs) from a knowledge graph. This method takes as input a knowledge graph and extracts EODPs as sets of axioms/constraints involving the classes instantiated in the KG. These EODPs include data about the probability of such axioms/constraints happening. We apply our method on two domain-specific portions of Wikidata, addressing the music and art, architecture, and archaeology domains, and we compare the empirical ontology design patterns we extract with the current support present in Wikidata. We show how these patterns can provide guidance for the use of the Wikidata ontology and its potential improvement, and can give insight into the content of (domain-specific portions of) the Wikidata knowledge graph.

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“…However, as highlighted in [76], all existing approaches supporting the automatic creation of shapes generate a great number of shape constraints such that it may be difficult to verify their soundness and completeness. Moreover, most generated shapes do not suggest specific classes that the objects of a property should be member of.…”

Section: Identifying Patterns Emerging From Knowledge Graphsmentioning

confidence: 99%

“…However, a limit to the number of the SPARQL query results needs to be specified, when working with large KGs such as Wikidata. [76] performs some experiments that show that the existing methods cannot handle the scale of large knowledge graphs like Wikidata, indeed they crash even with KGs with a few millions triples 3 . sheXer [31] is an automatic shape extractor that extracts shapes, which are serialised in both ShEx and SHACL, by mining the graph structure and exploring the neighborhood of specific target nodes.…”

Section: Related Workmentioning

confidence: 99%