Step-by-Step: Try the App with the Provided Dataset
Hint: In case you are interested in more details, there is a Help button in the bottom-right corner, with some more explainations of each step (help texts appear at the top of the page).
Get to know the dataset:
1. Open the readme file and the csv file (inside the zip) and briefly familiarize yourself with the dataset
Screen 1:
1. Go to the tool
2. Upload the dataset zip file and the readme you downloaded earlier.
3. Select the domain: Gardening
3. Enter this description in the provided text field:
-> "A data log about growing edible plants in different locations"
4. Click Submit
Screen 2:
1. Keep the Ontology Settings at their default values, except:
Enter 5 in the "Number of Entities to Extract" field (optionally also run it with 15 and 20).
KONDA now tries to find the 5 best Named Entities and Categories that can describe the dataset. The details get increased if we increase the number of named entities to extract.
2. Enter "plant" in the field "Search for Ontologies" and select the Ontology "cgo".
3. Click Submit
Screen 3:
KONDA has now found 5 named entities and suggested broader categories for each one.
1. Click on one category to see other suggestions.
2. Make sure that the suggestions make sense for the entity - change them if they do not.
All values here are plain text and you can also write your own entities or categories.
KONDA then tries to find fitting ontology terms later on.
3. Click Submit
Screen 4:
KONDA has now found 5 relations between the named entities and categories.
1. Click on one relation to see other suggestions.
2. Make sure that the suggestions make sense as a relation - change them if they do not
Again the relations here are just plain text which you can edit.
3. Click Submit
Screen 5:
KONDA is now trying to map your plain text entities, categories and relations to actual ontology terms.
You can see the entities and categories in blue, while the relations are displayed in red.
1. Again we need to make sure that all mappings are correct here
2. If one does not fit, click on the ontology term input field and try to search for a more fitting term
3. Find a blue match that exactly fits the ontology term (e.g. "Crop" matched to "Crop") and select the blue checkbox to replace the custom label with the ontology term.
(remember the name of the ontology term for the next screen)
4. Click Submit
Screen 6:
You have now created a Knowledge Graph.
1. Explore the graph displayed at the top.
- At the center, you'll see your dataset (yellow node).
- It's connected to your context files (purple nodes) via a "has context file" link.
- The dataset is also connected to the main concepts (blue nodes, named entities) using "has concept" links.
- Each concept may be connected to a broader category (orange node) with a "has broader" link.
- Concepts (blue) and categories (orange) can also be linked to ontology terms (green nodes) using "has related match".
2. Click on a blue node and notice the custom URI that was created.
3. Notice how blue and orange nodes are connected to green nodes.
4. Find the node that you have changed in the step before
5. Notice that this node has no "has related match" connection. Instead the label and URI was directly taken from the ontology term and replaced the custom label and URI.
6. Scroll to the bottom of the screen and export your knowledge graph, by selecting Turtle as the format.