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isislab-unisa · Apr 18, 2024 · c33e9a9 · c33e9a9
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diff --git a/...ull/all-metrics-multiple-observations.csv → ...ull/all-metrics-multiple-observations.csv b/...ull/all-metrics-multiple-observations.csv → ...ull/all-metrics-multiple-observations.csv
diff --git a/From csv to KG/Full/all-metrics.csv → ... csv (ESWC Workshop)/Full/all-metrics.csv b/From csv to KG/Full/all-metrics.csv → ... csv (ESWC Workshop)/Full/all-metrics.csv
diff --git a/From csv to KG/Full/not-all-metrics.csv → ... (ESWC Workshop)/Full/not-all-metrics.csv b/From csv to KG/Full/not-all-metrics.csv → ... (ESWC Workshop)/Full/not-all-metrics.csv
diff --git a/From csv to KG/Full/only-availability.csv → ...ESWC Workshop)/Full/only-availability.csv b/From csv to KG/Full/only-availability.csv → ...ESWC Workshop)/Full/only-availability.csv
diff --git a/... csv to KG/Full/only-related-to-trust.csv → ... Workshop)/Full/only-related-to-trust.csv b/... csv to KG/Full/only-related-to-trust.csv → ... Workshop)/Full/only-related-to-trust.csv
diff --git a/From csv to KG/Light/all-metrics-light.csv → ...SWC Workshop)/Light/all-metrics-light.csv b/From csv to KG/Light/all-metrics-light.csv → ...SWC Workshop)/Light/all-metrics-light.csv
diff --git a/...ll-metrics-multiple-observation-light.csv → ...ll-metrics-multiple-observation-light.csv b/...ll-metrics-multiple-observation-light.csv → ...ll-metrics-multiple-observation-light.csv
diff --git a/...csv to KG/Light/not-all-metrics-light.csv → ...Workshop)/Light/not-all-metrics-light.csv b/...csv to KG/Light/not-all-metrics-light.csv → ...Workshop)/Light/not-all-metrics-light.csv
diff --git a/From csv to KG/Light/only-availability.csv → ...SWC Workshop)/Light/only-availability.csv b/From csv to KG/Light/only-availability.csv → ...SWC Workshop)/Light/only-availability.csv
diff --git a/... KG/Light/only-related-to-trust-light.csv → ...op)/Light/only-related-to-trust-light.csv b/... KG/Light/only-related-to-trust-light.csv → ...op)/Light/only-related-to-trust-light.csv
diff --git a/From csv to KG/README.md → ...ate KG from csv (ESWC Workshop)/README.md b/From csv to KG/README.md → ...ate KG from csv (ESWC Workshop)/README.md
diff --git a/From csv to KG/dqv.ttl → Generate KG from csv (ESWC Workshop)/dqv.ttl b/From csv to KG/dqv.ttl → Generate KG from csv (ESWC Workshop)/dqv.ttl
diff --git a/README.md b/README.md
@@ -14,7 +14,7 @@ KGHeartBeat is a tool that can help you to analyze the quality of all Knowledge
     - [Results](#results)
     - [Look directly the quality](#look-directly-the-quality)
 - [How include a new quality metric?](#how-include-a-new-quality-metric)
-- [How can I get quality data as a Knowledge Graph?](./From%20csv%20to%20KG/)
+- [Generate RDF graph from csv (ESWC Workshop)](./Generate%20KG%20from%20csv%20(ESWC%20Workshop)/)
 
 ## Repository structure
 ```
@@ -143,4 +143,4 @@ You can view the quality data of each analysis performed in csv format in the fo
 Or, you can view the computed data through graphs and tables from our web-app: [KGHeartBeat-WebApp](http://www.isislab.it:12280/kgheartbeat/).
 
 ## How include a new quality metric?
-If you want to include a new quality metric, you need to include the calculation inside the [analyses.py](analyses.py) module. If this new metric requires the use of a new query on the SPARQL endpoint, you can add a new query in the [query.py](query.py) module and call it from the [analyses.py](analyses.py) module .Then, based on the quality dimension to which it belongs, modify the related class in the [QualityDimensions](/QualityDimensions/) folder, or create a new class if this belongs to a new dimension. If you created a new dimension for the new metric, it must be included in the [KnowledgeGraph.py](KnowledgeGraph.py) class. Then instantiate the classes in the [analyses.py](analyses.py) to assign the value obtained from the new quality metric. If you want also to see this new metric in the csv file given in output, you need to edit the [OutputCSV.py](OutputCSV.py) module appropriately. Essentially you have to include a new header, having as name the nameof the new metric and then recall the value of the metric from the [KnowledgeGraph.py](KnowledgeGraph.py) object appropriately constructed in the [analyses.py](analyses.py) module.
+If you want to include a new quality metric, you need to include the calculation inside the [analyses.py](analyses.py) module. If this new metric requires the use of a new query on the SPARQL endpoint, you can add a new query in the [query.py](query.py) module and call it from the [analyses.py](analyses.py) module .Then, based on the quality dimension to which it belongs, modify the related class in the [QualityDimensions](/QualityDimensions/) folder, or create a new class if this belongs to a new dimension. If you created a new dimension for the new metric, it must be included in the [KnowledgeGraph.py](KnowledgeGraph.py) class. Then instantiate the classes in the [analyses.py](analyses.py) to assign the value obtained from the new quality metric. If you want also to see this new metric in the csv file given in output, you need to edit the [OutputCSV.py](OutputCSV.py) module appropriately. Essentially you have to include a new header, having as name the name of the new metric and then recall the value of the metric from the [KnowledgeGraph.py](KnowledgeGraph.py) object appropriately constructed in the [analyses.py](analyses.py) module.