LabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance

Alper, Pinar; Belhajjame, Khalid; Goble, Carole; Karagöz, Pınar

doi:10.1007/978-3-319-16462-5_7

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…In the architecture discussion, three papers were presented: noWorkflow [80] is a tool that transparently captures provenance of scripts and enables reproducibility, LabelFlow [46] uses domain-specific labels with workflow provenance as a platform for data artifacts labelling; and [84] proposes software provenance to be included as part of software packages.…”

Section: Provenancementioning

confidence: 99%

Provenance in Open Data Entity-Centric Aggregation

Giunchiglia

Reyad

2015

Lecture Notes in Computer Science

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An increasing number of web services these days require combining data from several data providers into an aggregated database. Usually this aggregation is based on the linked data approach. On the other hand, the entity-centric model is a promising data model that outperforms the linked data approach because it solves the lack of explicit semantics and the semantic heterogeneity problems. However, current open data which is available on the web as raw datasets can not be used in the entity-centric model before processing them with an import process to extract the data elements and insert them correctly in the aggregated entity-centric database. It is essential to certify the quality of these imported data elements, especially the background knowledge part which acts as input to semantic computations, because the quality of this part affects directly the quality of the web services which are built on top of it. Furthermore, the aggregation of entities and their attribute values from different sources raises three problems: the need to trace the source of each element, the need to trace the links between entities which can be considered equivalent and the need to handle possible conflicts between different values when they are imported from various data sources. In this thesis, we introduce a new model to certify the quality of a back ground knowledge base which separates linguistic and language independent elements. We also present a pipeline to import entities from open data repositories to add the missing implicit semantics and to eliminate the semantic heterogeneity. Finally, we show how to trace the source of attribute values coming from different data providers; how to choose a strategy for handling possible conflicts between these values; and how to keep the links between identical entities which represent the same real world entity.

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Section: Provenancementioning

confidence: 99%

Provenance in Open Data Entity-Centric Aggregation

Giunchiglia

Reyad

2015

Lecture Notes in Computer Science

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“…Provenance Explorer was designed to provide a customizable visualization of the provenance trail associated with scientific discovery processes by utilizing both explicit and implicit RDF relationships [130]. LabelFlow is a tool to manipulate the workflow provenance of scientific data in RDF [131]. It enables semi-automated provenance annotation and can handle PROV-O-and WFPROV-compliant provenance traces.…”

Section: Software Tools For Manipulating Rdf Provenancementioning

confidence: 99%

Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs

Sikos

Philp

2020

Data Sci. Eng.

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Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications. Moreover, the analysis of the mechanisms and their limitations highlighted in this paper can serve as the basis for novel approaches in RDF-powered applications with increasing provenance needs.

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“…Regarding the third problem, it has long been recognized that semantic labeling is a central automation task for the semantic web, as users tend to avoid the extra manual work involved. For this purpose, it has been suggested that the provenance information contained in workflows can be used to add semantic labels to the nodes in such a workflow (Alper et al 2014). For the geospatial domain, it was demonstrated that the information contained in GIS workflows can be used to enrich geodata as well as GIS tools with important semantic types by traversing such a workflow, and share this information as linked data (Scheider and Ballatore 2018).…”

Section: Service Metadata Computational Core Concepts Linked Data Amentioning

confidence: 99%

Geospatial Information Processing Technologies

Gui

Hofer

et al. 2019

Manual of Digital Earth

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The increasing availability of geospatial data offers great opportunities for advancing scientific discovery and practices in society. Effective and efficient processing of geospatial data is essential for a wide range of Digital Earth applications such as climate change, natural hazard prediction and mitigation, and public health. However, the massive volume, heterogeneous, and distributed nature of global geospatial data pose challenges in geospatial information processing and computing. This chapter introduces three technologies for geospatial data processing: highperformance computing, online geoprocessing, and distributed geoprocessing, with each technology addressing one aspect of the challenges. The fundamental concepts, principles, and key techniques of the three technologies are elaborated in detail, followed by examples of applications and research directions in the context of Digital Earth. Lastly, a Digital Earth reference framework called discrete global grid system (DGGS) is discussed.

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LabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance

Cited by 9 publications

References 21 publications

Provenance in Open Data Entity-Centric Aggregation

Provenance in Open Data Entity-Centric Aggregation

Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs

Geospatial Information Processing Technologies

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