descriptions updated

README.md

@@ -1,8 +1,8 @@
 ### SSHOC Multilingual Data Stewardship terminology and Multilingual Metadata SKOSifying mapping
 
-This Jupyter Notebook implements a simple parser used to transform the SSHOC Multilingual Data Stewardship Terminology (http://hdl.handle.net/20.500.11752/ILC-567) and the SSHOC Multilingual Metadata (http://hdl.handle.net/20.500.11752/ILC-568), created in task 3.1 of the Social Sciences & Humanities Open Cloud (SSHOC) project, into SKOS resources.
+These Jupyter Notebooks implement a parser used to transform the [SSHOC Multilingual Data Stewardship Terminology](http://hdl.handle.net/20.500.11752/ILC-567) and the [SSHOC Multilingual Metadata](http://hdl.handle.net/20.500.11752/ILC-568), created during the Social Sciences & Humanities Open Cloud (SSHOC) project, into SKOS resources.
 
-The parser transforms the content in SKOS data following a set of mapping rules, the result is stored in *Turtle* and *rdf* files.
+The parsers transform the content in SKOS data following a set of mapping rules, the result is stored in *Turtle* and *rdf* files.
 
 ##### Installation
 It is recommended to install this notebook in a virtual environment to avoid dependency clash. 

rules.yaml

@@ -10,7 +10,7 @@ prefixes:
 mappings:
   conceptscheme:
     sources:
-      - ['data.json~jsonpath', '$']
+      - ['this~jsonpath', '$']
     s: skos:_ 
     po:
      - [a, skos:conceptScheme]
@@ -20,7 +20,7 @@ mappings:
     
   concept:
     sources:
-      - ['data.json~jsonpath', '$.concepts[*]']
+      - ['this~jsonpath', '$.concepts[*]']
     s: sshoccmd:$(ConceptId)
     po:
       - [a, skos:Concept]

sshoc_31_skos-yarrrml.ipynb

@@ -7,8 +7,7 @@
    "source": [
     "## Mapping *SSHOC Multilingual Metadata* to SKOS resources \n",
     "\n",
-    "This Notebook implements a simple parser used to transform the SSHOC Multilingual Metadata, created in the Task 3.1 of the SSHOC project and published as spreadsheets, into SKOS resources. The parser reads the spreadsheet and transforms the content in SKOS data following a set of mapping rules defined using [YRRRML](https://rml.io/yarrrml/) , the result is stored in Turtle files, and downloaded in a Fuseki server.\n",
-    "\n"
+    "This Notebook implements a simple parser used to transform the SSHOC Multilingual Metadata, created in the Task 3.1 of the SSHOC project and published as spreadsheet, into a SKOS resource. The parser reads the spreadsheet and transforms the content following a set of mapping rules defined using [YRRRML](https://rml.io/yarrrml/) , the result is stored in Turtle files, and downloaded in a Fuseki server."
    ]
   },
   {

sshoc_31_skos.ipynb

@@ -161,7 +161,7 @@
     {
      "data": {
       "text/plain": [
-       "<Graph identifier=N7b4b69840f2f484db36d3362b518a9b0 (<class 'rdflib.graph.Graph'>)>"
+       "<Graph identifier=Nfbc9b96ef4c047d9a72e68d22f8fc441 (<class 'rdflib.graph.Graph'>)>"
       ]
      },
      "execution_count": 7,
@@ -217,18 +217,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "failing-relative",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4577\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for index, row in df_data.iterrows():\n",
     "    \n",
@@ -317,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "earlier-slovak",
    "metadata": {},
    "outputs": [],
@@ -336,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "treated-spotlight",
    "metadata": {},
    "outputs": [],
@@ -355,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "olive-archive",
    "metadata": {},
    "outputs": [],
@@ -366,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "square-michael",
    "metadata": {},
    "outputs": [],
@@ -389,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "patient-winner",
    "metadata": {},
    "outputs": [],
@@ -409,21 +401,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "least-waterproof",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Graph identifier=N55bbc6f024264113946ce17315badffc (<class 'rdflib.graph.Graph'>)>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "now = datetime.datetime.today()\n",
     "today_date=now.date()\n",
@@ -461,18 +442,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "confirmed-montana",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3494\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "topconcepts=[]\n",
     "for index, row in df_metadata.iterrows():\n",
@@ -539,7 +512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "greater-thunder",
    "metadata": {},
    "outputs": [],
@@ -573,7 +546,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.11.1"
   }
  },
  "nbformat": 4,