Lop

Abstract

The Web of Data has emerged as a means to expose, share, reuse, and connect information on the Web identified by URIs using RDF as a data model, following Linked Data Principles. However, the reuse of third party data can be compromised without proper data quality assessments. In this context, important questions emerge: how can one trust on published data and links? Which manipulation, modification and integration operations have been applied to the data before its publication? What is the nature of comparisons or transformations applied to data during the interlinking process? In this scenario, provenance becomes a fundamental element. In this paper, we describe an approach for generating and capturing Linked Open Provenance (LOP) to support data quality and trustworthiness assessments, which covers preparation and format transformation of traditional data sources, up to dataset publication and interlinking. The proposed architecture takes advantage of provenance agents, orchestrated by an ETL workflow approach, to collect provenance at any specified level and also link it with its corresponding data. We also describe a real use case scenario where the architecture was implemented to evaluate the proposal.

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

“…Without sufficient transparency for Linked Data sources and transformations, government agencies and scientists cannot 66 https://franz.com/agraph/support/documentation/6.1.6/tripleattributes.html 67 https://neo4j.com 68 https://github.com/plus-provenance/plus 69 https://virtuoso.openlinksw.com 70 http://d2rq.org/d2r-server trust third-party LOD datasets [82]. The data interlinking mechanism used by LOD datasets can be utilized for coarse provenance information in the form of data associations, but these do not cover data transformations [108]. The owl:sameAs predicate, which is widely deployed among LOD datasets [109], can result in the confusion of provenance and ground truth [110].…”

Semantic Enrichment for Local Search Engine using Linked Open Data

“…The proliferation of the use of the Web of Data and the increasing amount of Linked Open Data (LOD) constitute a rich environment that stimulates the development of new applications and the wider consumption of published data [6]. The LOD cloud makes it possible to semantically enrich unstructured usergenerated content with structured information presented in the LOD resources.…”

“…In [2] they used semantic annotations to enrich the document metadata and provide new types of visualizations in an information retrieval context for identifying bibliographic references in texts. In [6] they describe an approach for generating and capturing Linked Open Provenance (LOP) to support data quality and trustworthiness assessment.…”