Fix docs

docs/examples/abox/1_csv.md

@@ -1,5 +1,9 @@
 # CSV file with metadata and time series
 
+```{note}
+Please follow [this link here](https://github.com/MI-FraunhoferIWM/data2rdf/blob/b29be66cb57beef8bd8f84e2cd588ccb8e17559c/examples/1_csv.ipynb) in order to access the related jupyter notebook.
+```
+
 ## General understanding
 
 In this example, we want to transfor a csv file which encorporates stress/strain of the measurement and some metadata about the experiment into an RDF repesentation.
@@ -71,6 +75,15 @@ Since we are assuming to have a csv file, we can assume the following parser arg
 * `"fillna"`: The value to fill NaN values in the parsed dataframe.
     In this example, we assume that the NaN values in the dataframe are filled with `""`. Hence the argument is `""`. This is in particular of importance when the time series is parsed from the csv file. Since we are using pandas to parse the csv file, we need to make sure that gaps in the time series are filled with `""`, instead of the default `np.nan` values in the dataframe. If not applied here, this might lead to problems in the data2rdf pipeline.
 
+The according parser args hence will look like this:
+
+```{python}
+parser_args = {
+      "metadata_sep":"\t",
+      "time_series_sep":"\t",
+      "metadata_length":20
+   }
+```
 
 ### The mapping
 
@@ -154,6 +167,43 @@ For the quantitative properties (e.g. `"Vorkraft"`=Preload) and non-quantitative
     "iri": "https://w3id.org/steel/ProcessOntology/TestStandard",
     "key": "Pr\u00fcfnorm"
   },
+  {
+    "iri": "https://w3id.org/steel/ProcessOntology/TestTime",
+    "key": "Pr\u00fcfzeit"
+  },
+  {
+    "iri": "https://w3id.org/steel/ProcessOntology/StandardForce",
+    "key": "Standardkraft"
+  },
+  {
+    "iri": "https://w3id.org/steel/ProcessOntology/Extension",
+    "key": "Standardweg"
+  },
+  {
+    "iri": "https://w3id.org/steel/ProcessOntology/Temperature",
+    "key": "Temperatur"
+  },
+  {
+    "iri": "https://w3id.org/steel/ProcessOntology/AbsoluteCrossheadTravel",
+    "key": "Traversenweg absolut"
+  },
+  {
+    "iri": "https://w3id.org/steel/ProcessOntology/ParallelLength",
+    "key": "Versuchsl\u00e4nge"
+  },
+  {
+    "iri": "https://w3id.org/steel/ProcessOntology/Preload",
+    "key": "Vorkraft"
+  },
+  {
+    "iri": "https://w3id.org/steel/ProcessOntology/DisplacementTransducer",
+    "key": "Wegaufnehmer"
+  },
+  {
+    "annotation": "https://w3id.org/steel/ProcessOntology",
+    "iri": "https://w3id.org/steel/ProcessOntology/Material",
+    "key": "Werkstoff"
+  }
 ]
 ```
 </Details>
@@ -456,7 +506,7 @@ pipeline = Data2RDF(
 
 The mapping can also be provided as a csv or excel file (**only as a file writen to disk, not as a string in memory**):
 
-```{csv}
+```
 key;iri;annotation
 Prüfinstitut;https://w3id.org/steel/ProcessOntology/TestingFacility;
 Projektnummer;https://w3id.org/steel/ProcessOntology/ProjectNumber;

docs/examples/abox/2_excel.md

@@ -1,5 +1,10 @@
 # Excel file with metadata and time series
 
+```{note}
+Please follow [this link here](https://github.com/MI-FraunhoferIWM/data2rdf/blob/b29be66cb57beef8bd8f84e2cd588ccb8e17559c/examples/2_excel.ipynb) in order to access the related jupyter notebook.
+```
+
+
 ```{note}
 This example is building up on the previous one about the [CSV file with metadata and time series](1_csv.md).
 Please start from this chapter in order to fully understand the content of this example.

docs/examples/abox/3_json.md

@@ -1,5 +1,10 @@
 # JSON file or Python-dict with metadata and time series
 
+```{note}
+Please follow [this link here](https://github.com/MI-FraunhoferIWM/data2rdf/blob/b29be66cb57beef8bd8f84e2cd588ccb8e17559c/examples/3_json.ipynb) in order to access the related jupyter notebook.
+```
+
+
 ```{note}
 This example is building up on the very first one about the [CSV file with metadata and time series](1_csv.md).
 Please start from this chapter in order to fully understand the content of this example.
@@ -27,7 +32,7 @@ We are considering the following dummy data as json input:
 ```
 raw_data = {
   "data": {
-    "Breitenänderung": {
+    "Breitenaenderung": {
       "unit": "mm",
       "value": 1.0
     },
@@ -67,7 +72,7 @@ A valid mapping for the json defined above may look like this:
   {
     "iri": "https://w3id.org/steel/ProcessOntology/WidthChange",
     "key": "Breitenaenderung",
-    "unit": "data.Breitenaenderung.unit",
+    "unit_location": "data.Breitenaenderung.unit",
     "value_location": "data.Breitenaenderung.value"
   },
   {

docs/examples/abox/4_csv_wo_metadata.md

@@ -1,5 +1,9 @@
 # CSV file without metadata
 
+```{note}
+Please follow [this link here](https://github.com/MI-FraunhoferIWM/data2rdf/blob/b29be66cb57beef8bd8f84e2cd588ccb8e17559c/examples/4_csv_wo_metadata.ipynb) in order to access the related jupyter notebook.
+```
+
 ## General understanding
 
 In this example, we are looking into dummy sensor data which is provided by a csv file. However, we do not have any metadata in this case, but directly start with the time series. Additionaly, each column in this time series is of the same ontological class, but was recorded by a different sensor.

docs/examples/abox/5_csv_w_na.md

@@ -1,5 +1,9 @@
 # CSV file without metadata and with missing values
 
+```{note}
+Please follow [this link here](https://github.com/MI-FraunhoferIWM/data2rdf/blob/b29be66cb57beef8bd8f84e2cd588ccb8e17559c/examples/5_csv_w_na.ipynb) in order to access the related jupyter notebook.
+```
+
 ## General understanding
 
 In comparision to the [previous example of the csv file without metadata](1.4_csv_wo_metadata.md), we are using the similar data again, but now we have some missing values in the time series. Again, there will be no metadata in this case.
@@ -17,7 +21,7 @@ For this example, we will consider the following inputs:
 
 For this example, we will consider the following input data:
 
-```{csv}
+```
 Temperature[°C];Coefficient of thermal exapansion[1/K];Specific heat[J/kgK];Young's modulus[Pa];Poison's ratio[-];Thermal conductivity[W/mK];Density[kg/m3]
 20;8.70E-06;8.46E+02;7.47E+10;0.218;0.99;2.47E+03
 100;9.00E-06;8.70E+02;7.43E+10;0.223;1.06;

docs/examples/abox/6_custom_relations.md

@@ -1,5 +1,9 @@
 # Graph with custom relations
 
+```{note}
+Please follow [this link here](https://github.com/MI-FraunhoferIWM/data2rdf/blob/b29be66cb57beef8bd8f84e2cd588ccb8e17559c/examples/6_graph_custom_relations.ipynb) in order to access the related jupyter notebook.
+```
+
 ## General understanding
 
 In this very small example, we would like to generate a graph without quantitative data, but some other alpha-numeric properties, like names or labels. Additionally, we would like to set a custom relation like an annotation property or datatype property between the node of the individual and the data which we parse.

docs/examples/tbox/tbox.md

@@ -1,5 +1,9 @@
 # TBox generation from a Python dictionary
 
+```{note}
+Please follow [this link here](https://github.com/MI-FraunhoferIWM/data2rdf/blob/b29be66cb57beef8bd8f84e2cd588ccb8e17559c/examples/7_tbox.ipynb) in order to access the related jupyter notebook.
+```
+
 ## General understanding
 
 data2rdf is able to run the pipeline in tbox mode. In tbox mode, data2rdf generates a class hierarchy of the provided data.