Add Knowledge Mapper page to documentation

docs/docs/get-started/knowledge-base.md → docs/docs/get-started/knowledge-base/knowledge-base.md

@@ -7,12 +7,9 @@ This page describes how to implement your own Knowledge Base given a data source
 There are three approaches to implement your own Knowledge Base:
 1. Java
 2. REST Developer API
-3. Knowledge Mapper (based on a Python client)
+3. [Knowledge Mapper](./knowledge-mapper.md) (based on a Python client)
 4. JavaScript client
 
-The Knowledge Mapper is a tool we have built to easily connect to several common data sources (SQL, RDF, APIs).
-If you're interested in using the Knowledge Mapper or JavaScript client, please reach out to us as they are not yet open source.
-
 ## Implementing your own Knowledge Interaction
 When you receive a request for data via an ANSWER or REACT Knowledge Interaction, you should return the expected results, e.g. by retrieving data from an API.
 

docs/docs/get-started/knowledge-base/knowledge-mapper.md

@@ -0,0 +1,61 @@
+---
+sidebar_position: 3
+---
+# Knowledge Mapper
+The Knowledge Mapper makes it easier to connect to common data sources including SQL, SPARQL and Python classes (e.g. to connect to APIs).
+It allows your Knowledge Base to be connected to the network using a single configuration file.
+The mapper takes care of connecting to the Knowledge Network and helps in registering your Knowledge Base and Knowledge Interactions.
+
+It is openly available at https://github.com/TNO/knowledge-mapper.
+
+## What's included?
+- Software to easily connect to SQL, SPARQL and Python classes (e.g. to connect to APIs)
+- Python client to connect to a Knowledge Network
+- Web interface to initialize a Knowledge Base that loads static JSON data
+
+## How to use it
+Examples of how to use the Knowledge Mapper to a SQL/SPARQL/Python data source are available at https://github.com/TNO/knowledge-mapper/tree/main/mapper/examples.
+
+### Typical project setup
+In a project that uses the Knowledge Mapper to connect to a network, you'll typically find the following files:
+```
+my-project
+├── .gitignore      
+├── Dockerfile      # For easy deployment
+├── README.md
+└── config.jsonc    # Configuration file
+```
+When connecting to a Python class, you'll also find either a `src/` directory or a `.py` file.
+
+The configuration file is important as it defines which Knowledge Interactions your connector provides to the network.
+
+### Configuration file
+Below you can find what such a configuration file looks like.
+Depending on whether you're connecting to SQL/SPARQL/Python, some additional variables need to be defined (see [examples](https://github.com/TNO/knowledge-mapper/tree/main/mapper/examples) for more details).
+
+```json
+{
+  // The endpoint where a knowledge engine runtime is available.
+  "knowledge_engine_endpoint": "http://tke-runtime:8280/rest",
+  "knowledge_base": {
+    // An URL representing the identity of this knowledge base
+    "id": "https://example.org/a-custom-knowledge-base",
+    // A name for this knowledge base
+    "name": "Some knowledge base",
+    // A description for this knowledge base
+    "description": "This is just an example."
+  },
+
+  // Several knowledge interaction definitions can be placed here
+  "knowledge_interactions": [
+    {
+      // The type of this knowledge interaction. If we have knowledge available that is requestable, the type should be "answer"
+      "type": "answer",
+      // The graph pattern that expresses the 'shape' of our knowledge
+      "pattern": "?tree <https://example.org/hasHeight> ?height . ?tree <https://example.org/hasName> ?name .",
+      // An optional name of this knowledge interaction
+      "name": "answer-trees-with-heights-and-names"
+    }
+  ]
+}
+```