SpellingCorrection

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

SpellingCorrection has 93 facts recorded in Dontopedia across 13 references, with 13 live disagreements.

93 facts·59 predicates·13 sources·13 in dispute

Mostly:rdf:type(12), has method(4), has parameter(3)

Rdf:typein dispute`rdf:type`

Text Processing^[2]all time · 28ff3364 2017 4558 946d 63674a03e0f4
Class^[3]all time · 56e5350d 9b8b 4765 A6c5 D324a644b00f
Process^[4]all time · E24dc3e9 D3c9 4c87 9eb2 F49f89b411ff
Class^[5]all time · 731b8e8a 1f12 4ab1 A853 9852e66bc19e
Domain^[6]all time · 1c4ae2ba D800 475c Bcb9 7ae83c1a31d3
Function^[7]all time · 679660b6 E3c2 4219 8f8c 2598b5c9e898
Technical Topic^[8]all time · 6da40d00 6d2d 43d3 Bd9f Ac89c0a9d73a
Function^[9]sourceall time · 0ce45954 3cc1 4c1f Bb57 028ef0f12e0e
Python Function^[10]all time · 23b7eaff D608 466b B7fe 551b05041bbb
Process^[11]all time · C336df37 Ebf1 4638 8f10 D3374f9d13ce

Inbound mentions (34)

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.

`memberOf`Member of(4)

Correct Spellingex:correct-spelling
Dictionaryex:dictionary
Initex:__init__
Load Dictionaryex:load-dictionary

`topic`Topic(3)

Code Example Requestex:code-example-request
Proof of Concept Queryex:proof-of-concept-query
Technical Mentoring Sessionex:technical-mentoring-session

`appliesTo`Applies to(2)

Class Instantiabilityex:class-instantiability
Potential Extensionex:potential-extension

`algorithmStep`Algorithm Step(1)

Correct Spellingex:correct-spelling

`assignedBy`Assigned by(1)

Corrected Textex:corrected-text

`attributeOf`Attribute of(1)

Dictionaryex:dictionary

`canBeFineTuned`Can Be Fine Tuned(1)

Pre Trained Modelsex:pre-trained-models

`demonstratesTask`Demonstrates Task(1)

Example Codeex:example-code

`describes`Describes(1)

Comment 2ex:comment-2

`domain`Domain(1)

Hunspell Libraryex:Hunspell-library

`duplicateOf`Duplicate of(1)

Final Validationex:final-validation

`hasComponent`Has Component(1)

Spelling Correction Systemex:spelling-correction-system

`hasFunction`Has Function(1)

Python Codeex:python-code

`hasIdenticalImplementation`Has Identical Implementation(1)

Final Validationex:final-validation

`instanceOf`Instance of(1)

Correction Moduleex:correction-module

`instantiates`Instantiates(1)

Correction Moduleex:correction-module

`intendedFor`Intended for(1)

Correct Query Functionex:correct-query-function

`isComponentOf`Is Component of(1)

Correct Tokenex:correct-token

`isDependencyOf`Is Dependency of(1)

Word Tokenizeex:word-tokenize

`isIdenticalTo`Is Identical to(1)

Final Validationex:final-validation

`isStartFor`Is Start for(1)

Pyspellcheckerex:pyspellchecker

`is-used-for`Is Used for(1)

Levenshtein Distanceex:levenshtein-distance

`methodOf`Method of(1)

Correct Spellingex:correct-spelling

`occursDuring`Occurs During(1)

Spell Check Errorex:SpellCheckError

`pipelineStage`Pipeline Stage(1)

Spelling Correctionex:spelling-correction

`purpose`Purpose(1)

Proof of Conceptex:proof-of-concept

`requiredBy`Required by(1)

Library Dependencyex:library-dependency

`usedFor`Used for(1)

Context Windowex:context-window

Other facts (76)

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.

76 facts

Predicate	Value	Ref
Has Method	Correct Spelling	[3]
Has Method	Init	[5]
Has Method	Load Dictionary	[5]
Has Method	Correct Spelling	[5]
Has Parameter	Tokens	[7]
Has Parameter	Dictionary	[7]
Has Parameter	Input Text	[10]
Returns	Corrected Tokens	[7]
Returns	Corrected Text String	[9]
Returns	String	[10]
Functionality	Tokenize Input Text	[9]
Functionality	Apply Correction Rules	[9]
Functionality	Join Corrected Tokens	[9]
Calls	Correct Token	[9]
Calls	Word Tokenize	[10]
Calls	Correct Token	[10]
Has Step	Tokenize Input Text	[9]
Has Step	Apply Correction Rules	[9]
Has Step	Join Corrected Tokens	[9]
Appends	Closest Match	[7]
Appends	Token	[7]
Append Operation	Append Closest Match	[7]
Append Operation	Append Original Token	[7]
Shares Variable Name	Corrected Tokens	[7]
Shares Variable Name	Tokens	[7]
Parameter Type	List of Strings	[7]
Parameter Type	Dictionary	[7]
Processes	Input Text	[9]
Processes	Tokens	[10]
Has Goal	Correct Target Word	[1]
Has Attribute	Dictionary	[3]
Programming Language	Python	[3]
Contains Method	Correct Spelling	[3]
Python Class Definition	class SpellingCorrection:	[3]
Is Instance Method	Correct Spelling	[3]
Python Syntax	class SpellingCorrection:	[3]
Transforms	Misspelled Words	[4]
Produces	Corrected Words	[4]
Is Custom Class	true	[5]
Not From Standard Library	true	[5]
Function Body	Correction Loop	[7]
Contains Loop	Token Iteration	[7]
Uses Condition	Dictionary Membership Check	[7]
Uses Function	Distance	[7]
Uses Min	Dictionary Keys	[7]
Uses Lambda	Distance Callback	[7]
Has Identical Implementation	Final Validation	[7]
Algorithm	Closest Match Correction	[7]
Pipeline Stage	Spelling Correction	[7]
Has Comment	No Comment	[7]
Control Flow	For Each Loop	[7]
Uses Conditional	If Not in Dictionary	[7]
Variable Assignment	Corrected Tokens	[7]
Function Definition	Def Statement	[7]
Uses Min Function	Min Builtin	[7]
Min Argument	Dictionary Keys Call	[7]
Min Key	Lambda Parameter	[7]
Lambda Body	Distance Call	[7]
Duplicate of	Final Validation	[7]
Loop Variable	Token Variable	[7]
Calls Function	Distance Function	[7]
Indented Level	4	[7]
Logic Flow	Check Then Correct	[7]
Parameter	Input Text	[9]
Uses	Word Tokenize	[9]
Inverse of	Text Generation	[9]
Design Pattern	Transform Filter Join	[9]
Uses Pattern	List Comprehension Pattern	[9]
Performs	Transform Process	[9]
Depends on	Correct Token	[9]
Has Dependency	Word Tokenize	[9]
Describes	Spelling Correction Process	[10]
Joins	Space Separator	[10]
Tokenizes	Input Text	[10]
Outputs	Corrected String	[10]
Iterates	Tokens	[10]

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.

93 undated

—

hasGoalbeam/59f386eb-3423-49c1-b803-c55da998bdde

ex:correct-target-word

—

typebeam/28ff3364-2017-4558-946d-63674a03e0f4

ex:TextProcessing

—

typebeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

ex:Class

—

labelbeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

SpellingCorrection

—

hasAttributebeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

ex:dictionary

—

hasMethodbeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

ex:correct-spelling

—

programmingLanguagebeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

Python

—

containsMethodbeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

ex:correct-spelling

—

pythonClassDefinitionbeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

class SpellingCorrection:

—

isInstanceMethodbeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

ex:correct-spelling

—

pythonSyntaxbeam/56e5350d-9b8b-4765-a6c5-d324a644b00f

class SpellingCorrection:

—

typebeam/e24dc3e9-d3c9-4c87-9eb2-f49f89b411ff

ex:Process

—

transformsbeam/e24dc3e9-d3c9-4c87-9eb2-f49f89b411ff

ex:misspelled-words

—

producesbeam/e24dc3e9-d3c9-4c87-9eb2-f49f89b411ff

ex:corrected-words

—

typebeam/731b8e8a-1f12-4ab1-a853-9852e66bc19e

ex:Class

—

labelbeam/731b8e8a-1f12-4ab1-a853-9852e66bc19e

SpellingCorrection

—

hasMethodbeam/731b8e8a-1f12-4ab1-a853-9852e66bc19e

ex:__init__

—

hasMethodbeam/731b8e8a-1f12-4ab1-a853-9852e66bc19e

ex:load-dictionary

—

hasMethodbeam/731b8e8a-1f12-4ab1-a853-9852e66bc19e

ex:correct-spelling

—

isCustomClassbeam/731b8e8a-1f12-4ab1-a853-9852e66bc19e

true

—

notFromStandardLibrarybeam/731b8e8a-1f12-4ab1-a853-9852e66bc19e

true

—

typebeam/1c4ae2ba-d800-475c-bcb9-7ae83c1a31d3

ex:Domain

—

labelbeam/1c4ae2ba-d800-475c-bcb9-7ae83c1a31d3

spelling correction

—

typebeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:Function

—

hasParameterbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:tokens

—

hasParameterbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:dictionary

—

returnsbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:corrected-tokens

—

functionBodybeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:correction-loop

—

containsLoopbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:token-iteration

—

usesConditionbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:dictionary-membership-check

—

usesFunctionbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:distance

—

usesMinbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:dictionary-keys

—

usesLambdabeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:distance-callback

—

appendsbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:closest-match

—

appendsbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:token

—

hasIdenticalImplementationbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:final-validation

—

algorithmbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:closest-match-correction

—

pipelineStagebeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:spelling-correction

—

hasCommentbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:no-comment

—

controlFlowbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:for-each-loop

—

usesConditionalbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:if-not-in-dictionary

—

variableAssignmentbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:corrected-tokens

—

functionDefinitionbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:def-statement

—

usesMinFunctionbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:min-builtin

—

minArgumentbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:dictionary-keys-call

—

minKeybeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:lambda-parameter

—

lambdaBodybeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:distance-call

—

appendOperationbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:append-closest-match

—

appendOperationbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:append-original-token

—

sharesVariableNamebeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:corrected-tokens

—

sharesVariableNamebeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:tokens

—

duplicateOfbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:final-validation

—

loopVariablebeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:token-variable

—

callsFunctionbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:distance-function

—

indentedLevelbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

—

logicFlowbeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:check-then-correct

—

parameterTypebeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:list-of-strings

—

parameterTypebeam/679660b6-e3c2-4219-8f8c-2598b5c9e898

ex:dictionary

—

typebeam/6da40d00-6d2d-43d3-bd9f-ac89c0a9d73a

ex:TechnicalTopic

—

parameterbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:input-text

—

functionalitybeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:tokenize-input-text

—

functionalitybeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:apply-correction-rules

—

functionalitybeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:join-corrected-tokens

—

typebeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:Function

—

callsbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:correct-token

—

usesbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:word-tokenize

—

returnsbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:corrected-text-string

—

hasStepbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:tokenize-input-text

—

hasStepbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:apply-correction-rules

—

hasStepbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:join-corrected-tokens

—

processesbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:input-text

—

inverseOfbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:text-generation

—

designPatternbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:transform-filter-join

—

usesPatternbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:list-comprehension-pattern

—

performsbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:transform-process

—

dependsOnbeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:correct-token

—

hasDependencybeam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e

ex:word-tokenize

—

typebeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:Python_Function

—

labelbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

spelling_correction

—

hasParameterbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:input-text

—

returnsbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:string

—

describesbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:Spelling_Correction_Process

—

callsbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:word_tokenize

—

callsbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:correct-token

—

joinsbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:space-separator

—

tokenizesbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:input-text

—

processesbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:tokens

—

outputsbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:corrected-string

—

iteratesbeam/23b7eaff-d608-466b-b7fe-551b05041bbb

ex:tokens

—

typebeam/c336df37-ebf1-4638-8f10-d3374f9d13ce

ex:Process

—

typebeam/c9e2838c-b8a4-4591-969b-ee77610720de

ex:Task

—

labelbeam/c9e2838c-b8a4-4591-969b-ee77610720de

Spelling Correction

—

typebeam/cd1202e2-8ff4-46e7-b33d-4ac9df22522f

ex:specific-task

References (13)

13 references

[1]59f386eb 3423 49c1 B803 C55da998bdde1 fact
ctx:claims/beam/59f386eb-3423-49c1-b803-c55da998bdde
- full textbeam-chunk
  text/plain1018 Bdoc:beam/59f386eb-3423-49c1-b803-c55da998bdde
  Show excerpt
  # this is where I need help - how can I use the context window to correct the spelling of the target word? # I've tried using a simple dictionary-based approach, but it's not accurate enough # I've also tried using m…
[2]28ff3364 2017 4558 946d 63674a03e0f41 fact
ctx:claims/beam/28ff3364-2017-4558-946d-63674a03e0f4
- full textbeam-chunk
  text/plain1 KBdoc:beam/28ff3364-2017-4558-946d-63674a03e0f4
  Show excerpt
  self.context_window = 5 # considering 5 words before and after the target word self.common_misspellings = { 'loking': 'looking', 'improove': 'improve', 'spelng': 'spelling' } …
[3]56e5350d 9b8b 4765 A6c5 D324a644b00f9 facts
ctx:claims/beam/56e5350d-9b8b-4765-a6c5-d324a644b00f
[4]E24dc3e9 D3c9 4c87 9eb2 F49f89b411ff3 facts
ctx:claims/beam/e24dc3e9-d3c9-4c87-9eb2-f49f89b411ff
- full textbeam-chunk
  text/plain1 KBdoc:beam/e24dc3e9-d3c9-4c87-9eb2-f49f89b411ff
  Show excerpt
  correction_module.load_dictionary(dictionary_data) query = "I'm loking for a way to improove my spelng" corrected_query = correction_module.correct_spelling(query) print(corrected_query) # Output: "I'm looking for a way to improve my spel…
[5]731b8e8a 1f12 4ab1 A853 9852e66bc19e7 facts
ctx:claims/beam/731b8e8a-1f12-4ab1-a853-9852e66bc19e
[6]1c4ae2ba D800 475c Bcb9 7ae83c1a31d32 facts
ctx:claims/beam/1c4ae2ba-d800-475c-bcb9-7ae83c1a31d3
- full textbeam-chunk
  text/plain1 KBdoc:beam/1c4ae2ba-d800-475c-bcb9-7ae83c1a31d3
  Show excerpt
  - **Description**: Populate dictionary with words for spell correction. - **Estimated Duration**: 1 day - **Assignee**: Carol 4. **Create a task for "Implement basic spell correction"**: - **Summary**: Implement basic spell cor…
[7]679660b6 E3c2 4219 8f8c 2598b5c9e89835 facts
ctx:claims/beam/679660b6-e3c2-4219-8f8c-2598b5c9e898
[8]6da40d00 6d2d 43d3 Bd9f Ac89c0a9d73a1 fact
ctx:claims/beam/6da40d00-6d2d-43d3-bd9f-ac89c0a9d73a
- full textbeam-chunk
  text/plain1 KBdoc:beam/6da40d00-6d2d-43d3-bd9f-ac89c0a9d73a
  Show excerpt
  By using this function, you can easily compute the average error rate and the distribution of correction statuses for your dataset, providing better insights for your analysis. [Turn 10366] User: Kathryn and I are outlining 3 spelling corr…
[9]0ce45954 3cc1 4c1f Bb57 028ef0f12e0e18 facts
ctx:claims/beam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e
- full textbeam-chunk
  text/plain1 KBdoc:beam/0ce45954-3cc1-4c1f-bb57-028ef0f12e0e
  Show excerpt
  ### Suggestions for Improvement 1. **Robust Tokenization**: - Use a more sophisticated tokenization method to handle punctuation and special characters. 2. **Enhanced Correction Rules**: - Implement more comprehensive correction rul…
[10]23b7eaff D608 466b B7fe 551b05041bbb12 facts
ctx:claims/beam/23b7eaff-d608-466b-b7fe-551b05041bbb
- full textbeam-chunk
  text/plain1 KBdoc:beam/23b7eaff-d608-466b-b7fe-551b05041bbb
  Show excerpt
  # Ensure NLTK resources are downloaded nltk.download('punkt') # Example dictionary of valid words dictionary = {'hello', 'world', 'example', 'test', 'correction'} def levenshtein_distance(token1, token2): """Calculate Levenshtein dist…
[11]C336df37 Ebf1 4638 8f10 D3374f9d13ce1 fact
ctx:claims/beam/c336df37-ebf1-4638-8f10-d3374f9d13ce
- full textbeam-chunk
  text/plain1 KBdoc:beam/c336df37-ebf1-4638-8f10-d3374f9d13ce
  Show excerpt
  [Turn 10378] User: I've been tasked with providing latency statistics whenever I discuss query latency reduction, so I'd like to know how I can optimize the spelling correction module to achieve the best possible latency, considering the ad…
[12]C9e2838c B8a4 4591 969b Ee77610720de2 facts
ctx:claims/beam/c9e2838c-b8a4-4591-969b-ee77610720de
- full textbeam-chunk
  text/plain1 KBdoc:beam/c9e2838c-b8a4-4591-969b-ee77610720de
  Show excerpt
  1. **Hyperparameter Search**: Use grid search or random search to find the best hyperparameters. 2. **Learning Rate Scheduling**: Use learning rate schedulers like `ReduceLROnPlateau` or `CosineAnnealingLR`. ### 4. Ensemble Methods 1. **E…
[13]Cd1202e2 8ff4 46e7 B33d 4ac9df22522f1 fact
ctx:claims/beam/cd1202e2-8ff4-46e7-b33d-4ac9df22522f
- full textbeam-chunk
  text/plain1 KBdoc:beam/cd1202e2-8ff4-46e7-b33d-4ac9df22522f
  Show excerpt
  But I'm not sure if this is the best approach. Do you have any suggestions for how we could improve our spelling correction system? Maybe something that uses machine learning or natural language processing? ->-> 4,29 [Turn 10649] Assistant…

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SpellingCorrection

Rdf:typein disputerdf:type

Inbound mentions (34)

memberOfMember of(4)

topicTopic(3)

appliesToApplies to(2)

algorithmStepAlgorithm Step(1)

assignedByAssigned by(1)

attributeOfAttribute of(1)

canBeFineTunedCan Be Fine Tuned(1)

demonstratesTaskDemonstrates Task(1)

describesDescribes(1)

domainDomain(1)

duplicateOfDuplicate of(1)

hasComponentHas Component(1)

hasFunctionHas Function(1)

hasIdenticalImplementationHas Identical Implementation(1)

instanceOfInstance of(1)

instantiatesInstantiates(1)

intendedForIntended for(1)

isComponentOfIs Component of(1)

isDependencyOfIs Dependency of(1)

isIdenticalToIs Identical to(1)

isStartForIs Start for(1)

is-used-forIs Used for(1)

methodOfMethod of(1)

occursDuringOccurs During(1)

pipelineStagePipeline Stage(1)

purposePurpose(1)

requiredByRequired by(1)

usedForUsed for(1)

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Rdf:typein dispute`rdf:type`

`memberOf`Member of(4)

`topic`Topic(3)

`appliesTo`Applies to(2)

`algorithmStep`Algorithm Step(1)

`assignedBy`Assigned by(1)

`attributeOf`Attribute of(1)

`canBeFineTuned`Can Be Fine Tuned(1)

`demonstratesTask`Demonstrates Task(1)

`describes`Describes(1)

`domain`Domain(1)

`duplicateOf`Duplicate of(1)

`hasComponent`Has Component(1)

`hasFunction`Has Function(1)

`hasIdenticalImplementation`Has Identical Implementation(1)

`instanceOf`Instance of(1)

`instantiates`Instantiates(1)

`intendedFor`Intended for(1)

`isComponentOf`Is Component of(1)

`isDependencyOf`Is Dependency of(1)

`isIdenticalTo`Is Identical to(1)

`isStartFor`Is Start for(1)

`is-used-for`Is Used for(1)

`methodOf`Method of(1)

`occursDuring`Occurs During(1)

`pipelineStage`Pipeline Stage(1)

`purpose`Purpose(1)

`requiredBy`Required by(1)

`usedFor`Used for(1)