create dataintegration reference docs from endpoint

Author: seebi

docs/build/.pages

@@ -3,6 +3,7 @@ nav:
     - Introduction to the User Interface: introduction-to-the-user-interface
     - Rule Operators: rule-operators
     - Define Prefixes / Namespaces: define-prefixes-namespaces
+    - Task and Operator Reference: reference
     - Cool IRIs: cool-iris
     - Lift Tabular Data: lift-data-from-tabular-data-such-as-csv-xslx-or-database-tables
     - Lift Hierarchical Data: lift-data-from-json-and-xml-sources
@@ -14,4 +15,4 @@ nav:
     - Link Intrusion Detection Systems to Open-Source INTelligence: tutorial-how-to-link-ids-to-osint
     - Project and Global Variables: variables
     - Evaluate Template Operator: evaluate-template
-    - Build Knowledge Graphs from Kafka Topics: kafka-consumer
+    - Build Knowledge Graphs from Kafka Topics: kafka-consumer

docs/build/reference/.pages

@@ -0,0 +1,7 @@
+nav:
+    - "Task and Operator Reference": index.md
+    - "Aggregators": aggregator
+    - "Custom Workflow Tasks": customtask
+    - "Datasets": dataset
+    - "Distance Measures": distancemeasure
+    - "Transformers": transformer

docs/build/reference/aggregator/.pages

@@ -0,0 +1,11 @@
+nav:
+    - index.md
+    - "And": min.md
+    - "Average": average.md
+    - "Euclidian distance": quadraticMean.md
+    - "First non-empty score": firstNonEmpty.md
+    - "Geometric mean": geometricMean.md
+    - "Handle missing values": handleMissingValues.md
+    - "Negate": negate.md
+    - "Or": max.md
+    - "Scale": scale.md

docs/build/reference/aggregator/average.md

@@ -0,0 +1,43 @@
+---
+title: "Average"
+description: "Computes the weighted average."
+icon: octicons/cross-reference-24
+tags: 
+---
+# Average
+<!-- This file was generated - DO NOT CHANGE IT MANUALLY -->
+
+
+
+
+### Examples
+
+**Notation:** List of values are represented via square brackets. Example: `[first, second]` represents a list of two values "first" and "second".
+
+---
+#### Computes the arithmetic mean of all similarity scores:
+
+* Input values: [0.4, 0.5, 0.9]
+* Returns: `0.6`
+
+
+---
+#### Multiplies individual similarity scores with their weight before averaging:
+
+* Weights: [1, 1, 2]
+* Input values: [0.3, 0.5, 0.6]
+* Returns: `0.5`
+
+
+---
+#### Missing scores always lead to an output of none:
+
+* Input values: [-1.0, (none), 1.0]
+* Returns: `(none)`
+
+
+
+
+## Parameter
+
+`None`

docs/build/reference/aggregator/firstNonEmpty.md

@@ -0,0 +1,28 @@
+---
+title: "First non-empty score"
+description: "Forwards the first input that provides a non-empty similarity score."
+icon: octicons/cross-reference-24
+tags: 
+---
+# First non-empty score
+<!-- This file was generated - DO NOT CHANGE IT MANUALLY -->
+
+
+
+
+### Examples
+
+**Notation:** List of values are represented via square brackets. Example: `[first, second]` represents a list of two values "first" and "second".
+
+---
+#### The first defined score is returned, even if it's not the highest score:
+
+* Input values: [(none), 0.2, 0.5]
+* Returns: `0.2`
+
+
+
+
+## Parameter
+
+`None`

docs/build/reference/aggregator/geometricMean.md

@@ -0,0 +1,68 @@
+---
+title: "Geometric mean"
+description: "Compute the (weighted) geometric mean."
+icon: octicons/cross-reference-24
+tags: 
+---
+# Geometric mean
+<!-- This file was generated - DO NOT CHANGE IT MANUALLY -->
+
+
+
+
+### Examples
+
+**Notation:** List of values are represented via square brackets. Example: `[first, second]` represents a list of two values "first" and "second".
+
+---
+#### Example 1:
+
+* Weights: [1, 2, 1]
+* Input values: [0.0, 0.0, 0.0]
+* Returns: `0.0`
+
+
+---
+#### Example 2:
+
+* Weights: [1, 2, 1]
+* Input values: [1.0, 1.0, 1.0]
+* Returns: `1.0`
+
+
+---
+#### Example 3:
+
+* Weights: [2, 1]
+* Input values: [0.5, 1.0]
+* Returns: `0.629961`
+
+
+---
+#### Example 4:
+
+* Weights: [2, 1, 5]
+* Input values: [0.5, 1.0, 0.7]
+* Returns: `0.672866`
+
+
+---
+#### Example 5:
+
+* Weights: [10, 2, 3]
+* Input values: [0.1, 0.9, 0.2]
+* Returns: `0.153971`
+
+
+---
+#### Missing scores always lead to an output of none:
+
+* Input values: [-1.0, (none), 1.0]
+* Returns: `(none)`
+
+
+
+
+## Parameter
+
+`None`

docs/build/reference/aggregator/handleMissingValues.md

@@ -0,0 +1,46 @@
+---
+title: "Handle missing values"
+description: "Generates a default similarity score, if no similarity score is provided (e.g., due to missing values). Using this operator can have a performance impact, since it lowers the efficiency of the underlying computation."
+icon: octicons/cross-reference-24
+tags: 
+---
+# Handle missing values
+<!-- This file was generated - DO NOT CHANGE IT MANUALLY -->
+
+
+
+
+### Examples
+
+**Notation:** List of values are represented via square brackets. Example: `[first, second]` represents a list of two values "first" and "second".
+
+---
+#### Forwards input similarity scores:
+
+* Input values: [0.1]
+* Returns: `0.1`
+
+
+---
+#### Outputs the default score, if no input score is provided:
+
+* Parameters
+  * *defaultValue*: `1.0`
+
+* Input values: [(none)]
+* Returns: `1.0`
+
+
+
+
+## Parameter
+
+### Default value
+
+The default value to be generated, if no similarity score is provided. Must be a value between -1 (inclusive) and 1 (inclusive). '1' represents boolean true and '-1' represents boolean false.
+
+- Datatype: `double`
+- Default Value: `-1.0`
+
+
+

docs/build/reference/aggregator/index.md

@@ -0,0 +1,25 @@
+---
+title: "Aggregators"
+icon: octicons/cross-reference-24
+tags:
+    - Build
+    - Reference
+---
+# Aggregators
+<!-- This file was generated - DO NOT CHANGE IT MANUALLY -->
+
+This kind of task aggregates multiple similarity scores.
+
+**:octicons-people-24: Intended audience:** Linked Data Experts and Domain Experts
+
+|                    Name | Description |
+|------------------------:| :--------- |
+|[And](min.md) | All input scores must be within the threshold. Selects the minimum score. |
+|[Average](average.md) | Computes the weighted average. |
+|[Euclidian distance](quadraticMean.md) | Calculates the Euclidian distance. |
+|[First non-empty score](firstNonEmpty.md) | Forwards the first input that provides a non-empty similarity score. |
+|[Geometric mean](geometricMean.md) | Compute the (weighted) geometric mean. |
+|[Handle missing values](handleMissingValues.md) | Generates a default similarity score, if no similarity score is provided (e.g., due to missing values). Using this operator can have a performance impact, since it lowers the efficiency of the underlying computation. |
+|[Negate](negate.md) | Negates the result of the input comparison. A single input is expected. Using this operator can have a performance impact, since it lowers the efficiency of the underlying computation. |
+|[Or](max.md) | At least one input score must be within the threshold. Selects the maximum score. |
+|[Scale](scale.md) | Scales a similarity score by a factor. |

docs/build/reference/aggregator/max.md

@@ -0,0 +1,50 @@
+---
+title: "Or"
+description: "At least one input score must be within the threshold. Selects the maximum score."
+icon: octicons/cross-reference-24
+tags: 
+---
+# Or
+<!-- This file was generated - DO NOT CHANGE IT MANUALLY -->
+
+
+
+
+### Examples
+
+**Notation:** List of values are represented via square brackets. Example: `[first, second]` represents a list of two values "first" and "second".
+
+---
+#### Selects the maximum similarity score:
+
+* Input values: [0.5, 0.0]
+* Returns: `0.5`
+
+
+---
+#### Selects the maximum similarity score:
+
+* Input values: [-1.0, -0.5, -0.3]
+* Returns: `-0.3`
+
+
+---
+#### Missing scores default to a similarity score of -1:
+
+* Input values: [(none)]
+* Returns: `-1.0`
+
+
+---
+#### Weights are ignored:
+
+* Weights: [1000, 0]
+* Input values: [1.0, 0.0]
+* Returns: `1.0`
+
+
+
+
+## Parameter
+
+`None`

docs/build/reference/aggregator/min.md

@@ -0,0 +1,50 @@
+---
+title: "And"
+description: "All input scores must be within the threshold. Selects the minimum score."
+icon: octicons/cross-reference-24
+tags: 
+---
+# And
+<!-- This file was generated - DO NOT CHANGE IT MANUALLY -->
+
+
+
+
+### Examples
+
+**Notation:** List of values are represented via square brackets. Example: `[first, second]` represents a list of two values "first" and "second".
+
+---
+#### Selects the minimum similarity score:
+
+* Input values: [1.0, 0.0]
+* Returns: `0.0`
+
+
+---
+#### Selects the minimum similarity score:
+
+* Input values: [-1.0, 0.0, 0.5, 1.0]
+* Returns: `-1.0`
+
+
+---
+#### Missing scores default to a similarity score of -1:
+
+* Input values: [1.0, (none), -0.5]
+* Returns: `-1.0`
+
+
+---
+#### Weights are ignored:
+
+* Weights: [1000, 0]
+* Input values: [1.0, 0.0]
+* Returns: `0.0`
+
+
+
+
+## Parameter
+
+`None`