Manually Clean Dataset
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-07.)
Manually Clean Dataset has 18 facts recorded in Dontopedia across 4 references, with 5 live disagreements.
Mostly:rdf:type(4), describes action(2), corrects(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (10)
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.
comparedAgainstCompared Against(2)
- Openrefine Cleaning
ex:openrefine-cleaning - Openrefine Cleaning
ex:openrefine-cleaning
comparesMethodsCompares Methods(2)
- Comparison Method
ex:comparison-method - Comparison Step
ex:comparison-step
comparedToCompared to(1)
- Openrefine
ex:openrefine
compared-withCompared With(1)
- Openrefine
ex:openrefine
comparesMethodCompares Method(1)
- Openrefine Vs Manual Cleaning
ex:openrefine-vs-manual-cleaning
containsSectionContains Section(1)
- Explanation
ex:explanation
hasControlHas Control(1)
- Evaluation Design
ex:evaluation-design
hasStepHas Step(1)
- Workflow
ex:workflow
Other facts (16)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Manual Cleaning Method | [1] |
| Rdf:type | Baseline Method | [2] |
| Rdf:type | Process | [3] |
| Rdf:type | Data Cleaning Method | [4] |
| Describes Action | replacing 'Unknown' names | [3] |
| Describes Action | filling NaN ages | [3] |
| Corrects | Unknown Names | [3] |
| Corrects | Nan Values | [3] |
| Restores Column | Name Column | [3] |
| Restores Column | Age Column | [3] |
| Serves As | Ground Truth | [1] |
| Compared Against | Openrefine Cleaning | [1] |
| Replaces | Unknown String | [3] |
| Fills | Nan Ages | [3] |
| Compared to | Openrefine | [4] |
| Baseline for | Openrefine Cleaning | [4] |
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.
References (4)
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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