Document Types

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-07.)

Document Types has 19 facts recorded in Dontopedia across 7 references, with 3 live disagreements.

19 facts·7 predicates·7 sources·3 in dispute

Mostly:rdf:type(6), has category(5), includes(3)

Inbound mentions (5)

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.

`basedOn`Based on(2)

Corpus Strataex:corpus-strata
Strataex:strata

`assumesPresenceOf`Assumes Presence of(1)

Data Frameex:DataFrame

`contains`Contains(1)

Sampleex:sample

`containsAtLeast`Contains at Least(1)

Sampleex:sample

Other facts (18)

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.

18 facts

Predicate	Value	Ref
Rdf:type	Classification Category	[1]
Rdf:type	Classification Scheme	[2]
Rdf:type	Classification Labels	[3]
Rdf:type	Classification Category	[4]
Rdf:type		[5]
Rdf:type	File Categories	[7]
Has Category	Emails Stratum	[2]
Has Category	Reports Stratum	[2]
Has Category	Invoices Stratum	[2]
Has Category	Memos Stratum	[2]
Has Category	Other Stratum	[2]
Includes	Docx Document	[1]
Includes	Pdf Document	[1]
Includes	Txt Document	[1]
Part of	Data Frame	[3]
Distinct Count	10	[4]
Contained in	Sample	[5]
Is Basis for	Stratification	[6]

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.

19 undated

—

typebeam/fc72a4b8-eacf-4de5-91ee-138455d804d5

ex:ClassificationCategory

—

includesbeam/fc72a4b8-eacf-4de5-91ee-138455d804d5

ex:docx-document

—

includesbeam/fc72a4b8-eacf-4de5-91ee-138455d804d5

ex:pdf-document

—

includesbeam/fc72a4b8-eacf-4de5-91ee-138455d804d5

ex:txt-document

—

typebeam/1beb4978-4037-4cb3-b798-2b7033c17548

ex:ClassificationScheme

—

hasCategorybeam/1beb4978-4037-4cb3-b798-2b7033c17548

ex:emails-stratum

—

hasCategorybeam/1beb4978-4037-4cb3-b798-2b7033c17548

ex:reports-stratum

—

hasCategorybeam/1beb4978-4037-4cb3-b798-2b7033c17548

ex:invoices-stratum

—

hasCategorybeam/1beb4978-4037-4cb3-b798-2b7033c17548

ex:memos-stratum

—

hasCategorybeam/1beb4978-4037-4cb3-b798-2b7033c17548

ex:other-stratum

—

typebeam/8951974a-470b-4a56-8030-ad3ac43f8c5f

ex:ClassificationLabels

—

partOfbeam/8951974a-470b-4a56-8030-ad3ac43f8c5f

ex:DataFrame

—

distinctCountbeam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a

10

—

typebeam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a

ex:classification-category

—

typebeam/250f29db-74b8-42ea-a67b-a4cfadef49bf

ex:

—

labelbeam/250f29db-74b8-42ea-a67b-a4cfadef49bf

Document Types

—

containedInbeam/250f29db-74b8-42ea-a67b-a4cfadef49bf

ex:sample

—

isBasisForbeam/45af0c7a-a92b-45bf-b1f4-496260d16f7b

ex:stratification

—

typebeam/4b5ea8bc-d948-4098-a9af-81e7cfdb141f

ex:file-categories

References (7)

7 references

[1]Fc72a4b8 Eacf 4de5 91ee 138455d804d54 facts
ctx:claims/beam/fc72a4b8-eacf-4de5-91ee-138455d804d5
- full textbeam-chunk
  text/plain1 KBdoc:beam/fc72a4b8-eacf-4de5-91ee-138455d804d5
  Show excerpt
  1. **Prepare Your Test Data** - Ensure you have a diverse set of 300 documents covering different types (e.g., `.docx`, `.pdf`, `.txt`, etc.). - Place these documents in a designated directory. 2. **Define Success Criteria** - Det…
[2]1beb4978 4037 4cb3 B798 2b7033c175486 facts
ctx:claims/beam/1beb4978-4037-4cb3-b798-2b7033c17548
[3]8951974a 470b 4a56 8030 Ad3ac43f8c5f2 facts
ctx:claims/beam/8951974a-470b-4a56-8030-ad3ac43f8c5f
- full textbeam-chunk
  text/plain1 KBdoc:beam/8951974a-470b-4a56-8030-ad3ac43f8c5f
  Show excerpt
  from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # Assuming I have a DataFrame with document types and features df = pd.read_csv('documents.csv') # Split data into training and testing sets X_…
[4]924a6db5 B2b0 42d4 9e5c Bd5a7a159a3a2 facts
ctx:claims/beam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a
- full textbeam-chunk
  text/plain1 KBdoc:beam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a
  Show excerpt
  6. **Build Index**: Use Faiss to build an index of the document vectors. 7. **Search and Retrieve**: Encode the query into a vector, normalize it, and search the index to find the most similar documents based on cosine similarity. ### Conc…
[5]250f29db 74b8 42ea A67b A4cfadef49bf3 facts
ctx:claims/beam/250f29db-74b8-42ea-a67b-a4cfadef49bf
- full textbeam-chunk
  text/plain1 KBdoc:beam/250f29db-74b8-42ea-a67b-a4cfadef49bf
  Show excerpt
  By using statistical sampling and calculating a confidence interval, you can estimate the volume of documents in your corpus with a high degree of accuracy. The provided code ensures that the estimate is within a 90% confidence interval, pr…
[6]45af0c7a A92b 45bf B1f4 496260d16f7b1 fact
ctx:claims/beam/45af0c7a-a92b-45bf-b1f4-496260d16f7b
- full textbeam-chunk
  text/plain1 KBdoc:beam/45af0c7a-a92b-45bf-b1f4-496260d16f7b
  Show excerpt
  By using stratified sampling and weighted sampling, you can account for the variability in document sizes and improve the accuracy of your volume estimation. This approach ensures that each type of document is adequately represented in the …
[7]4b5ea8bc D948 4098 A9af 81e7cfdb141f1 fact
ctx:claims/beam/4b5ea8bc-d948-4098-a9af-81e7cfdb141f
- full textbeam-chunk
  text/plain1 KBdoc:beam/4b5ea8bc-d948-4098-a9af-81e7cfdb141f
  Show excerpt
  How can I improve the accuracy of the metadata to reach my target of 94%? ->-> 4,31 [Turn 4855] Assistant: To improve the accuracy of metadata extraction using Tika, you can consider several strategies. These include preprocessing the docu…

See also

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