Towards Versioning of Arbitrary RDF Data

Frommhold, Marvin; Piris, Rubén Navarro; Arndt, Natanael; Tramp, Sebastian; Petersen, Niklas; Martin, Michael

doi:10.1145/2993318.2993327

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…It follows a combined delta and snapshot storage approach. The system is comparable to the approach presented by Frommhold et al [21], as both systems are linear change tracking systems. None of the systems provides support for branches to allow independent evolution of RDF graphs.…”

Section: Practical Knowledge Base Versioning Systemsmentioning

confidence: 72%

Decentralized Collaborative Knowledge Management Using Git

Arndt

Naumann

Radtke

et al. 2019

Journal of Web Semantics

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The World Wide Web and the Semantic Web are designed as a network of distributed services and datasets. The distributed character of the Web brings manifold collaborative possibilities to interchange data. The commonly adopted collaborative solutions for RDF data are centralized (e. g. SPARQL endpoints and wiki systems). But to support distributed collaboration, a system is needed, that supports divergence of datasets, brings the possibility to conflate diverged states, and allows distributed datasets to be synchronized. In this paper, we present Quit Store, it was inspired by and it builds upon the successful Git system. The approach is based on a formal expression of evolution and consolidation of distributed datasets. During the collaborative curation process, the system automatically versions the RDF dataset and tracks provenance information. It also provides support to branch, merge, and synchronize distributed RDF datasets. The merging process is guarded by specific merge strategies for RDF data. Finally, we use our reference implementation to show overall good performance and demonstrate the practical usability of the system. (Edgard Marx) URL: http://aksw.org/NatanaelArndt (Natanael Arndt), http://aksw.org/NormanRadtke (Norman Radtke), http://aksw.org/MichaelMartin (Michael Martin), http://aksw.org/EdgardMarx (Edgard Marx) 1 http://lod-cloud.net/ 2

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Section: Practical Knowledge Base Versioning Systemsmentioning

confidence: 72%

Decentralized Collaborative Knowledge Management Using Git

Arndt

Naumann

Radtke

et al. 2019

Journal of Web Semantics

Self Cite

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“…Described isolation of JPS agents' operations on the knowledge graph within separate parallel worlds motivated the choice of "Independent Copies" as a default Archiving Policy. Certainly, from the pure data storage point of view, there are possible more efficient solutions, such as those based on theory of patches (Frommhold et al, 2016) and delta calculations (Berners-Lee and Connolly, 2004) or even multi-indexed and compressed delta chains (Taelman et al, 2019). However, they may not always be a first choice from the version creation and retrieval timing point of view, which is regarded as another key performance aspect of version management systems (Tzitzikas et al, 2008).…”

Section: The Parallel World Frameworkmentioning

confidence: 99%

A Parallel World Framework for scenario analysis in knowledge graphs

Eibeck

Chadzynski

Lim

et al. 2020

DCE

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This paper presents Parallel World Framework as a solution for simulations of complex systems within a time-varying knowledge graph and its application to the electric grid of Jurong Island in Singapore. The underlying modeling system is based on the Semantic Web Stack. Its linked data layer is described by means of ontologies, which span multiple domains. The framework is designed to allow what-if scenarios to be simulated generically, even for complex, inter-linked, cross-domain applications, as well as conducting multi-scale optimizations of complex superstructures within the system. Parallel world containers, introduced by the framework, ensure data separation and versioning of structures crossing various domain boundaries. Separation of operations, belonging to a particular version of the world, is taken care of by a scenario agent. It encapsulates functionality of operations on data and acts as a parallel world proxy to all of the other agents operating on the knowledge graph. Electric network optimization for carbon tax is demonstrated as a use case. The framework allows to model and evaluate electrical networks corresponding to set carbon tax values by retrofitting different types of power generators and optimizing the grid accordingly. The use case shows the possibility of using this solution as a tool for CO2 reduction modeling and planning at scale due to its distributed architecture.

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“…[16] introduces an RDF-based approach that supports versioning of RDF datasets and blank nodes, and uses an RDF vocabulary to describe changes to an RDF dataset. For each SPARQL update query, this system creates a patch containing the added and deleted triples and their corresponding RDF graphs.…”

Section: Related Workmentioning

confidence: 99%

Automating Data Citation: The eagle-i Experience

Alawini¹,

Chen²,

Davidson³

et al. 2017

2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Data citation is of growing concern for owners of curated databases, who wish to give credit to the contributors and curators responsible for portions of the dataset and enable the data retrieved by a query to be later examined. While several databases specify how data should be cited, they leave it to users to manually construct the citations and do not generate them automatically. We report our experiences in automating data citation for an RDF dataset called eagle-i, and discuss how to generalize this to a citation framework that can work across a variety of different types of databases (e.g. relational, XML, and RDF). We also describe how a database administrator would use this framework to automate citation for a particular dataset.

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Towards Versioning of Arbitrary RDF Data

Cited by 11 publications

References 9 publications

Decentralized Collaborative Knowledge Management Using Git

Decentralized Collaborative Knowledge Management Using Git

A Parallel World Framework for scenario analysis in knowledge graphs

Automating Data Citation: The eagle-i Experience

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