From cd09614ab2be485a796a572274d336c1c47ca4a9 Mon Sep 17 00:00:00 2001
From: Jules Belveze <32683010+JulesBelveze@users.noreply.github.com>
Date: Thu, 4 Aug 2022 08:42:38 +0200
Subject: [PATCH] chore: add 'concepCy' to spacy universe (#11255)

* chore: add 'concepCy' to spacy universe

* docs: add 'slogan' to concepCy
---
 website/meta/universe.json | 42 ++++++++++++++++++++++++++++++++++----
 1 file changed, 38 insertions(+), 4 deletions(-)

diff --git a/website/meta/universe.json b/website/meta/universe.json
index a128f0795..6c8caa6a6 100644
--- a/website/meta/universe.json
+++ b/website/meta/universe.json
@@ -1,5 +1,39 @@
 {
     "resources": [
+        {
+            "id": "concepcy",
+            "title": "concepCy",
+            "slogan": "A multilingual knowledge graph in spaCy",
+            "description": "A spaCy wrapper for ConceptNet, a freely-available semantic network designed to help computers understand the meaning of words.",
+            "github": "JulesBelveze/concepcy",
+            "pip": "concepcy",
+            "code_example": [
+                "import spacy",
+                "import concepcy",
+                "",
+                "nlp = spacy.load('en_core_web_sm')",
+                "# Using default concepCy configuration",
+                "nlp.add_pipe('concepcy')",
+                "",
+                "doc = nlp('WHO is a lovely company')",
+                "",
+                "# Access all the 'RelatedTo' relations from the Doc",
+                "for word, relations in doc._.relatedto.items():",
+                "    print(f'Word: {word}\n{relations}')",
+                "",
+                "# Access the 'RelatedTo' relations word by word",
+                "for token in doc:",
+                "    print(f'Word: {token}\n{token._.relatedto}')"
+            ],
+            "category": ["pipeline"],
+            "image": "https://github.com/JulesBelveze/concepcy/blob/main/figures/concepcy.png",
+            "tags": ["semantic", "ConceptNet"],
+            "author": "Jules Belveze",
+            "author_links": {
+                "github": "JulesBelveze",
+                "website": "https://www.linkedin.com/in/jules-belveze/"
+            }
+        },
         {
             "id": "spacyfishing",
             "title": "spaCy fishing",
@@ -2604,7 +2638,7 @@
                 "    Add the courgette, garlic, red peppers and oregano and cook for 2–3 minutes.",
                 "    Later, add some oranges and chickens.\"\"\"",
                 "",
-                "# use any model that has internal spacy embeddings", 
+                "# use any model that has internal spacy embeddings",
                 "nlp = spacy.load('en_core_web_lg')",
                 "nlp.add_pipe(\"concise_concepts\", ",
                 "    config={\"data\": data}",
@@ -2650,7 +2684,7 @@
                 "    At that location, Nissin was founded.",
                 "    Many students survived by eating these noodles, but they don't even know him.\"\"\"",
                 "",
-                "# use any model that has internal spacy embeddings", 
+                "# use any model that has internal spacy embeddings",
                 "nlp = spacy.load('en_core_web_sm')",
                 "nlp.add_pipe(",
                 "    \"xx_coref\", config={\"chunk_size\": 2500, \"chunk_overlap\": 2, \"device\": 0})",
@@ -2833,7 +2867,7 @@
                 "doc = nlp(\"AE died in Princeton in 1955.\")",
                 "",
                 "print(doc._.clauses)",
-                "# Output:",       
+                "# Output:",
                 "# <SV, AE, died, None, None, None, [in Princeton, in 1955]>",
                 "",
                 "propositions = doc._.clauses[0].to_propositions(as_text=True)",
@@ -3599,7 +3633,7 @@
                 "",
                 "#Lexico Semantic (LxSem) Features",
                 "TTRF = LingFeat.TTRF_() #Type Token Ratio Features",
-                "VarF = LingFeat.VarF_() #Noun/Verb/Adj/Adv Variation Features", 
+                "VarF = LingFeat.VarF_() #Noun/Verb/Adj/Adv Variation Features",
                 "PsyF = LingFeat.PsyF_() #Psycholinguistic Difficulty of Words (AoA Kuperman)",
                 "WoLF = LingFeat.WorF_() #Word Familiarity from Frequency Count (SubtlexUS)",
                 "",