• Corpus ID: 10867055

Methodology for Assessment of Linked Data Quality

@inproceedings{Rula2014MethodologyFA,
  title={Methodology for Assessment of Linked Data Quality},
  author={Anisa Rula and Amrapali Zaveri},
  booktitle={LDQ@SEMANTiCS},
  year={2014}
}
With the expansion in the amount of data being produced as Linked Data (LD), the opportunity to build use cases has also increased. However, a crippling problem to the reliability of these use cases is the underlying poor data quality. Moreover, the ability to assess the quality of the consumed LD, based on the satisfaction of the consumers’ quality requirements, signicantly inuences usability of such data for a given use case. In this paper, we propose a data quality assessment methodology… 

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References

SHOWING 1-10 OF 16 REFERENCES

Quality Assessment Methodologies for Linked Open Data A Systematic Literature Review and Conceptual Framework

A systematic review of approaches for assessing the data quality of LOD is presented and a comprehensive list of the dimensions and metrics is presented to provide researchers and data curators a comprehensive understanding of existing work.

Sieve: linked data quality assessment and fusion

Sieve, a framework for flexibly expressing quality assessment methods as well as fusion methods for quality assessment and fusion, is presented, which is integrated into the Linked Data Integration Framework (LDIF), which handles Data Access, Schema Mapping and Identity Resolution.

User-driven quality evaluation of DBpedia

This study aims to assess the quality of this sample of DBpedia resources and adopt an agile methodology to improve the quality in future versions by regularly providing feedback to the DBpedia maintainers.

Test-driven evaluation of linked data quality

This work presents a methodology for test-driven quality assessment of Linked Data, which is inspired by test- driven software development, and argues that vocabularies, ontologies and knowledge bases should be accompanied by a number of test cases, which help to ensure a basic level of quality.

Linked Data: Evolving the Web into a Global Data Space

This Synthesis lecture provides readers with a detailed technical introduction to Linked Data, including coverage of relevant aspects of Web architecture, as the basis for application development, research or further study.

Quality-driven information filtering using the WIQA policy framework

Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)

When you read more every page of this data quality concepts methodologies and techniques data centric systems and applications, what you will obtain is something great.

ORE - A Tool for Repairing and Enriching Knowledge Bases

ORE supports the detection of a variety of ontology modelling problems and guides the user through the process of resolving them and allows to extend an ontology through (semi-)automatic supervised learning.

Refining Concepts in Description Logics

While the problem of learning logic programs has been extensively studied in ILP, the problem of learning in description logics (DLs) has been studied to a lesser extent. Learning in DLs is however