OpenRefine
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-07.)
OpenRefine has 19 facts recorded in Dontopedia across 7 references, with 2 live disagreements.
19 facts·8 predicates·7 sources·2 in dispute
Mostly:rdf:type(7), used for(2), is evaluated by(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (12)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
iteratesOverIterates Over(2)
- For Loop
ex:for-loop - Row Iteration
ex:row-iteration
appliesToApplies to(1)
- Len Openrefine
ex:len-openrefine
comparedToCompared to(1)
- Manual Cleaning
ex:manual-cleaning
comparesCompares(1)
- Comparison Task
ex:comparison-task
evaluatesEvaluates(1)
- Success Rate Metric
ex:success-rate-metric
isCleanedByIs Cleaned by(1)
- Metadata
ex:metadata
isOutputOfIs Output of(1)
- Openrefine Output.csv
ex:openrefine_output.csv
isPurposeOfIs Purpose of(1)
- Metadata Cleaning
ex:metadata-cleaning
targetSystemTarget System(1)
- Evaluate Openrefine Performance
ex:evaluate-openrefine-performance
toolTool(1)
- Process Step 3
ex:process-step-3
usesToolUses Tool(1)
- Openrefine Cleaning Step
ex:openrefine-cleaning-step
Other facts (15)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
15 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Data Cleaning Tool | [1] |
| Rdf:type | Data Cleaning Software | [1] |
| Rdf:type | Software | [2] |
| Rdf:type | Data Cleaning Tool | [3] |
| Rdf:type | Software Tool | [4] |
| Rdf:type | Tool | [6] |
| Rdf:type | Software Tool | [7] |
| Used for | Metadata Cleaning | [1] |
| Used for | data cleaning | [7] |
| Is Evaluated by | Success Rate Metric | [1] |
| Version | 3.7.0 | [2] |
| Used for | Metadata Cleaning | [5] |
| Compared With | Manual Cleaning | [5] |
| Is Subject of | Evaluation Guide | [5] |
| Compared to | Manual Cleaning | [7] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
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typebeam/e06228ca-08d1-403f-af94-242c605c308e
ex:DataCleaningTool
—
usedForbeam/e06228ca-08d1-403f-af94-242c605c308e
ex:metadata-cleaning
—
isEvaluatedBybeam/e06228ca-08d1-403f-af94-242c605c308e
ex:success-rate-metric
—
typebeam/e06228ca-08d1-403f-af94-242c605c308e
ex:DataCleaningSoftware
—
typebeam/4bf72c19-e147-4c83-b922-030035464495
ex:Software
—
labelbeam/4bf72c19-e147-4c83-b922-030035464495
OpenRefine
—
versionbeam/4bf72c19-e147-4c83-b922-030035464495
3.7.0
—
typebeam/f971d9d3-7050-4d32-844b-58db9f4972d7
ex:DataCleaningTool
—
typebeam/b00c301c-c592-4cd6-ad07-b1de426fb5c4
ex:SoftwareTool
—
used-forbeam/336f50f5-6e67-42bf-b2f1-406aa219718e
ex:metadata-cleaning
—
labelbeam/336f50f5-6e67-42bf-b2f1-406aa219718e
OpenRefine
—
compared-withbeam/336f50f5-6e67-42bf-b2f1-406aa219718e
ex:manual-cleaning
—
is-subject-ofbeam/336f50f5-6e67-42bf-b2f1-406aa219718e
ex:evaluation-guide
—
typebeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:Tool
—
labelbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
OpenRefine
—
typebeam/39688d70-2fa0-464e-b4cb-b00c300076b1
ex:SoftwareTool
—
labelbeam/39688d70-2fa0-464e-b4cb-b00c300076b1
OpenRefine
—
usedForbeam/39688d70-2fa0-464e-b4cb-b00c300076b1
data cleaning
—
comparedTobeam/39688d70-2fa0-464e-b4cb-b00c300076b1
ex:manual-cleaning
References (7)
7 references
ctx:claims/beam/e06228ca-08d1-403f-af94-242c605c308ectx:claims/beam/4bf72c19-e147-4c83-b922-030035464495ctx:claims/beam/f971d9d3-7050-4d32-844b-58db9f4972d7- full textbeam-chunktext/plain1 KB
doc:beam/f971d9d3-7050-4d32-844b-58db9f4972d7Show excerpt
Manually clean the dataset to create a reference for comparison. This step involves fixing the inconsistencies introduced in the previous step. ```python # Manually clean the dataset df_cleaned = df.copy() # Replace 'Unknown' names with o…
ctx:claims/beam/b00c301c-c592-4cd6-ad07-b1de426fb5c4- full textbeam-chunktext/plain970 B
doc:beam/b00c301c-c592-4cd6-ad07-b1de426fb5c4Show excerpt
This approach should help you identify the most common date formats in your dataset and pinpoint the root cause of the inconsistencies. [Turn 4500] User: I want to evaluate the performance of OpenRefine in cleaning metadata. Can you help m…
ctx:claims/beam/336f50f5-6e67-42bf-b2f1-406aa219718e- full textbeam-chunktext/plain1 KB
doc:beam/336f50f5-6e67-42bf-b2f1-406aa219718eShow excerpt
[Turn 4501] Assistant: Certainly! To evaluate the performance of OpenRefine in cleaning metadata, you can generate a test dataset with various inconsistencies and then compare the results of OpenRefine's cleaning against a manually cleaned …
ctx:claims/beam/abbe86bc-57a3-4347-aab0-645abb0507b7- full textbeam-chunktext/plain1 KB
doc:beam/abbe86bc-57a3-4347-aab0-645abb0507b7Show excerpt
# Define a function to compare the two datasets def compare_cleaning(openrefine, manual): # Calculate the number of matching entries matches = 0 for index, row in openrefine.iterrows(): if row.equals(manual.loc[index]): …
ctx:claims/beam/39688d70-2fa0-464e-b4cb-b00c300076b1- full textbeam-chunktext/plain1 KB
doc:beam/39688d70-2fa0-464e-b4cb-b00c300076b1Show excerpt
1. **Generate Test Dataset**: Run the first script to generate the test dataset and save it to `test_dataset.csv`. 2. **Manually Clean Dataset**: Run the second script to manually clean the dataset and save it to `manually_cleaned_dataset.c…
See also
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