KGHeartbeat

This repository behaves as a support material for the paper entitled "KGHeartBeat: a Knowledge Graph Quality
Management Tool" submitted (and under evaluation) as resource paper at ISWC 2023.

This is a python application that automatically analyzes the quality of Knowledge Graphs available on LODCloud and DataHUB. The quality categories analyzed by the application are:

• Accessibilithy
• Intrinsic
• Trust
• Dataset dynamicity
• Contextual
• Representational

Each of these categories is made up of quality dimensions. Overall, the tool analyzes 23 different quality dimensions, without the need of iteraction with the user.

Input configuration

From the configuration.json file, you can choose the Knowledge Graph to analyze. You can analyze it by using a list of keywords or ids. In the example below, all the Knowledge Graphs that have keywords museum will be analyzed.

{"name": ["museum"], "id": []}

Or, by a list of ids like this:

{"name": [], "id": ["dbpedia","taxref-ld"]}

If instead, you want to analyze all the Knowledge Graphs automatically discoverable from LODCloud and DataHub:

{"name": [], "id": []}

Start of the analysis

After the input configuration, to execute the analysis simply execute from the main directory of the project:

python manager.py

The content of this repository is also provided on GitHub at https://github.com/isislab-unisa/KGHeartbeat.

The entire project is released under the MIT license.