This webpage gives an overview of the results from the RDF Validation Survey,
specifically for the answers collected between December 2024 and March 2025.
The survey itself is still available
here.
All graphs have been generated based on the cleaned data available as
CSV files, or a ready-made DuckDB files on the associated Git repository. You can find the SQL queries retrieving the data for the graphs below
every graph. Try them out in our database shell directly in the browser: copy the SQL queries associated with the charts and paste them in the DuckDB browser shell!
This study is a direct result of the community's engagement, and we offer our warmest thanks to every participant who took the time to share their perspective!
Highlighted Insights
This section highlights key figures and tables as presented in the accompanying paper.
Professional Background
Professional Background of Respondents (2025 vs 2022)
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Application Domains
Main Application Domains for SHACL or ShEx
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Years of Experience
Years of Experience with RDF Validation/Shape Technologies by Professional Background
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Language Familiarity
Familiarity with RDF Validation Technologies
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Methods for Shape Creation
Comparison of Shape Creation Methods (2025 vs 2022)
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Validation Usage Frequency
Frequency of Working with RDF Shape Languages by Professional Background
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SHACL-SPARQL Usage Frequency
Frequency of Using SPARQL-based Constraint Components
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SHACL-SPARQL Motivation
Motivation for Using SPARQL-based Constraint Components
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Methods for Validating Evolving KGs
Adapting Validation Processes for Evolving Knowledge Graphs
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Graph Size vs. Performance Concerns
Performance Concerns in Relation to Graph Size at Validation Time
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Frequency of Use vs. Advanced Features
Usage of Advanced SHACL Features Based on Overall Usage Frequency
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Shape Generation/Extraction Tools
Tools Used for Shape Generation/Extraction (2025 vs 2022)
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SHACL Validators
SHACL Validation Software/Framework Usage by Professional Background
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Advanced SHACL Feature Usage
Usage of Advanced SHACL Features by Professional Background
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Graph Size at Validation Time
Typical RDF Graph Size at Validation Time by Professional Background
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Perceived Advantages
Main Perceived Advantages of SHACL/ShEx
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Reported Issues and Limitations
Reported Issues, Limitations, and Desired Enhancements
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Validation Report Usefulness
Features to Improve Usefulness of Validation Reports
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Desired Future Language Features
Desired Future Extensions of SHACL Standard
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Overview
This overview shows the number of respondents per answer for each of the questions from the original survey.
1 Background and Demographics
Question 1: What is your professional background?
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Question 2: How many years of experience do you have with RDF validation/shape technologies?
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Question 3: Do you work with different graph models or RDF standards? Please select all that apply.
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2 Usage Context and Specifics
Question 4: What RDF validation technologies do you have experience with? Please select all that apply.
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Question 5: How frequently do you work with RDF shape languages?
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Question 6: What are the main data domains you use SHACL or ShEx for? Please select all that apply.
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Question 7: In your projects, how do SHACL and ShEx affect data quality and consistency?
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Question 8: If you had to choose one language for future projects, which would it be?
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3 Validation Usage
Question 9: When validating, how many distinct classes and properties does your data graph usually have?
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Question 10: When validating, how large is your RDF graph usually?
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Question 11: If you are a user of SHACL, which validation software/framework do you use?
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Question 12: If you are a user of ShEx, which validation software/framework do you use?
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Question 13: What do you consider the main advantages of SHACL/ShEx in your work or projects? Please select all that apply.
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Question 14: Have you encountered any limitations or challenges when using SHACL/ShEx?
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Question 15: Have you worked with evolving or dynamic RDF knowledge graphs? If yes, how do you adapt your validation processes?
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4 Specific Features of SHACL
Question 16: How often do you use SHACL-core constraint components (for example sh:mincount etc)?
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Question 17: Do you use SPARQL-based constraint components?
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Question 18: If you use SPARQL-based constraint components, please specify the reason:
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Question 19: Is the information provided by the validation report sufficient for your use cases?
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Question 20: Which of the SHACL advanced features do you use?
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Question 21: Which extensions of the SHACL standard do you wish for?
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5 Shape Creation and Extraction
Question 22: How do you generate validating shapes SHACL/ShEx?
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Question 23: Which tools or method do you use to extract/create validating shapes (SHACL/ShEx)?
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Question 24: How many shapes do you usually generate?
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Question 25: How large is your ontology usually in the context of shape extraction?
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Question 26: How many distinct predicate types are present in your RDF graph usually?
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Question 27: How large is your RDF graph usually in the context of shape extraction?
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Question 28: Do you generate shapes for the entire graph or only for some portions?
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6 Future Development
Question 29: What are the shortcomings you experienced when working with SHACL/ShEx? Please select all that apply.
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Question 30: What features would improve the usefulness of validation reports? Please select all that apply.
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Methodology
The online survey targets practitioners, researchers,
and developers experienced with RDF data validation, SHACL, or ShEx. The data was collected from December 2024 to March 2025 and the survey was
administered anonymously using Google Forms to ensure participant privacy.
Distribution of the survey was achieved through a multi-channel approach to reach a broad yet relevant audience. We leveraged social media
platforms including the SHACL community Discord group, and the authors' LinkedIn and Twitter profiles. The survey was additionally disseminated via
the Semantic Web mailing list. A targeted outreach was performed by executing a DBLP query to identify authors of relevant publications;
these authors were then manually contacted via email (if mentioned in the papers) and invited to participate. As we make use of a completely anonymous survey structure, the results rely on the honesty of the respondents.
The questionnaire was designed for flexibility. Most questions were optional and featured closed-ended answer options, often allowing
for multiple selections. To capture richer, more nuanced feedback, some questions also included an option for free-text responses.
When trends were observed, they were additionally coded as part of the available dataset.