Capturing Relational Schemas and Functional Dependencies in RDFS

Calvanese, Diego; Fischl, Wolfgang; Pichler, Reinhard; Sallinger, Emanuel; Šimkus, Mantas

doi:10.1609/aaai.v28i1.8867

Cited by 17 publications

(3 citation statements)

References 7 publications

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“…E.g., extensions of DL-Lite + R with role functionality would be very useful in the context of graph structured data. Generalizing the positive decidability results to logics with powerful identification constraints, like the ones considered in (Calvanese et al 2014), would also be of practical importance. Given that the considered problems are intractable even for weak fragments of the core DL-Lite and very restricted forms of actions, it remains to explore how feasible these tasks are in practice, and whether there are meaningful restrictions that make them tractable.…”

Section: Discussionmentioning

confidence: 99%

Managing Change in Graph-Structured Data Using Description Logics

Ahmetaj

Calvanese

Ortiz

et al. 2014

AAAI

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In this paper we consider the setting of graph-structured data that evolves as a result of operations carried out by users or applications. We study different reasoning problems, which range from ensuring the satisfaction of a given set of integrity constraints after a given sequence of updates, to deciding the (non-)existence of a sequence of actions that would take the data to an (un)desirable state, starting either from a specific data instance or from an incomplete description of it. We consider a simple action language in which actions are finite sequences of insertions and deletions of nodes and labels, and use Description Logics for describing integrity constraints and (partial) states of the data. We then formalize the data management problems mentioned above as a static verification problem and several planning problems. We provide algorithms and tight complexity bounds for the formalized problems, both for an expressive DL and for a variant of DL-Lite.

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Section: Discussionmentioning

confidence: 99%

Managing Change in Graph-Structured Data Using Description Logics

Ahmetaj

Calvanese

Ortiz

et al. 2014

AAAI

Self Cite

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“…Inference of constraints, as well as SHACL shapes, from other data formalisms has been studied as well. Calvanese et al [9] and Sequeda et al [30] consider inference of RDFS and OWL, respectively, from direct mappings [2] between relational data and RDF. Similarly, Thapa and Giese [33] consider inferences of SHACL shapes from direct mappings, while RML2SHACL [12] allows the translation of RML rules to SHACL shapes.…”

Section: Related Workmentioning

confidence: 99%

From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries

Seifer,

Hernández,

Lämmel

et al. 2024

Proceedings of the ACM Web Conference 2024

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SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes allowing users to understand which data they can expect and which not. However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data: Shape constraints on the input graph may affect the output graph, but may no longer apply literally, and new shapes may be imposed by the query template. In this paper, we study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. We assume that the SPARQL CONSTRUCT query is fixed, e.g., being part of a program, whereas the input graphs adhere to input shape constraints but may otherwise vary over time and, thus, are mostly unknown. We study a fragment of SPARQL CONSTRUCT queries (SCCQ) and a fragment of SHACL (Simple SHACL). We formally define the problem of deriving the most restrictive set of Simple SHACL shapes that constrain the results from evaluating a SCCQ over any input graph restricted by a given set of Simple SHACL shapes. We propose and implement an algorithm that statically analyses input SHACL shapes and CONSTRUCT queries and prove its soundness and complexity.

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“…Quite some work is done in bringing functional dependencies (FD) to knowledge graphs, e.g. (Akhtar et al, 2010;Calvanese et al, 2014;Hellings et al, 2016). A FD X → Y expresses that entities with the same values for all attributes in X must also have the same values for those in Y .…”

Section: Related Workmentioning

confidence: 99%

Bottom-up Discovery of Context-aware Quality Constraints for Heterogeneous Knowledge Graphs

Wilcke

Kleijn

Boer

et al. 2020

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Capturing Relational Schemas and Functional Dependencies in RDFS

Cited by 17 publications

References 7 publications

Managing Change in Graph-Structured Data Using Description Logics

Managing Change in Graph-Structured Data Using Description Logics

From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries

Bottom-up Discovery of Context-aware Quality Constraints for Heterogeneous Knowledge Graphs

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