"Methodology for Assessment of Linked Data Quality: A Framework" at Workshop on Linked Data Quality Paper: https://dl.dropboxusercontent.com/u/2265375/LDQ/ldq2014_submission_3.pdf

1. A Methodology for
Assessment of
Linked Data Quality
Anisa Rula
Amrapali Zaveri

2. Outline
➢Linked Data Quality
○ Current State
○ Limitations
➢Quality Assessment Methodology
○ 3 phases, 6 steps
➢Conclusion
○ Future Work

3. Linked Data Quality
● c.a. 50 Billion Facts in
the Linked Data Cloud
● But, what about the
quality?
● Data is only as good
as its quality !

4. Linked Data Quality
➢30 approaches, 18 Dimensions, 69 Metrics*
➢12 Tools
○ Automated
○ Semi-automated
➢No generalized methodology
➢Not taking into account the actual use case/user
requirements
➢Only assessment, no improvement
* http://www.semantic-web-journal.net/content/quality-assessment-linked-data-survey

5. Quality
Assessment
Methodology
for Linked Data
➢3 Phases
➢6 steps

6. Phase I: Requirement Analysis
Step I: Use Case Analysis
- Description that best illustrates the intended
usage of the dataset(s)
Two types of users
➢Consumers
➢Potential consumers

7. Phase II: Quality Assessment
Step II: Identification of quality issues
➢Based on the use case
➢Checklist-based approach
➢Yes - 1, No - 0
➢List of quality dimensions

8. Phase II: Quality Assessment
Step III: Statistics and Low-level
Analysis
➢Generic statistics
➢Example
○ Interlinking degree
○ Blank nodes

9. Phase II: Quality Assessment
Step IV: Advanced Analysis
➢High-level metrics
➢Example
○ Accuracy
○ Completeness
➢Requires (i) input and (ii) target dataset

10. Data Quality Score
➢Ratio
○ DQscore = 1 - (V/T)
■ V - total no. of instances that violate a DQ rule
■ T - total no. of relevant instances
■ for each property
○ DQweightedscore= (DQscore * wi / W)
■ wi - weight
■ W - sum of all weighted factors of the properties
■ for quality of overall properties

11. Phase III: Quality Improvement
Step V: Root Cause Analysis
➢Analyze cause of each quality issue
➢Helps user interpret the results
➢Detect whether the problem occurs in the
original dataset
➢In case original dataset is unavailable,
analyze the available dataset to determine
the cause

12. Phase III: Quality Improvement
Step VI: Fixing Quality Problems
➢Semi-automatic
○ Consistency
○ Completeness
○ Syntactic validity
➢Crowdsourcing*
○ Semantic accuracy
○ Datatypes
○ Interlinks
* Acosta et al., Crowdsourcing Linked Data Quality Assessment. ISWC 2013.

13. Conclusion and Future Work
➢Assessment methodology - 3 phases, 6
steps
➢Focus on use case
➢Improvement phase
!
Future Work
➢Application to an actual use case
➢Build a tool

14. Thank you
Questions
Suggestions
Comments
@AnisaRula
@amrapaliz