ClimateKG Graph Visualisation

Generates graph visualisations of the ClimateKG knowledge graph by querying the PROD Wikibase SPARQL endpoint.

Each section provides: - A copy-paste SPARQL query for the Wikibase Query UI (switch display to Graph mode) - A direct deep-link to open the query in the UI - A Python-rendered version using pyvis / networkx for offline use

Wikibase Query UI: https://prod-climatekg.semanticclimate.org/query/
SPARQL endpoint: https://prod-climatekg.semanticclimate.org/query/proxy/sparql

📓 Download Notebook: graph-visualisation.ipynb

Graph visualisation mode in the Wikibase Query UI

After running any query in the UI, click the display type button (bottom-left, default is Table) and select Graph.
The graph builder reads ?item as the source node and ?linkTo as the target node.
Node labels come from ?itemLabel / ?linkToLabel.

Setup

Show code

from SPARQLWrapper import SPARQLWrapper, JSON
import pandas as pd
from IPython.display import display, Markdown, HTML
import urllib.parse

SPARQL_ENDPOINT = "https://prod-climatekg.semanticclimate.org/query/proxy/sparql"
QUERY_UI_BASE   = "https://prod-climatekg.semanticclimate.org/query/#"
WIKIBASE_URL    = "https://prod-climatekg.semanticclimate.org"

def run_query(sparql_query: str) -> pd.DataFrame:
    """Execute a SPARQL SELECT query and return results as a DataFrame."""
    sparql = SPARQLWrapper(SPARQL_ENDPOINT)
    sparql.setQuery(sparql_query)
    sparql.setReturnFormat(JSON)
    results = sparql.query().convert()
    bindings = results["results"]["bindings"]
    if not bindings:
        return pd.DataFrame()
    return pd.DataFrame([
        {k: v["value"] for k, v in row.items()}
        for row in bindings
    ])

def query_ui_link(sparql_query: str, label: str = "Open in Wikibase Query UI") -> str:
    """Return a Markdown link that opens the query in the Wikibase Query UI."""
    url = QUERY_UI_BASE + urllib.parse.quote(sparql_query, safe='')
    return f"[{label}]({url})"

print(f"Endpoint: {SPARQL_ENDPOINT}")

Endpoint: https://prod-climatekg.semanticclimate.org/query/proxy/sparql

1. Corpus Hierarchy Graph

Shows the Part of (P3) relationships that form the corpus backbone: Chapter → Text Division → Report → Report Series → Work

This is the most readable full-data graph: every node is a real Wikibase item, every edge is a P3 triple.

Tip: In the Query UI, switch to Graph display mode after running.

Show code

HIERARCHY_QUERY = f"""
PREFIX wd:   <{WIKIBASE_URL}/entity/>
PREFIX wdt:  <{WIKIBASE_URL}/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?item ?itemLabel ?linkTo ?linkToLabel WHERE {{
  ?item wdt:P3 ?linkTo .

  # Only include corpus-layer items (Work / Report Series / Report / Text Division / Chapter)
  VALUES ?allowedClass {{ wd:Q2 wd:Q3 wd:Q4 wd:Q5 wd:Q6 }}
  ?item  wdt:P1 ?allowedClass .

  OPTIONAL {{ ?item   rdfs:label ?itemLabel   . FILTER(LANG(?itemLabel)   = "en") }}
  OPTIONAL {{ ?linkTo rdfs:label ?linkToLabel . FILTER(LANG(?linkToLabel) = "en") }}
}}
ORDER BY ?linkToLabel ?itemLabel
"""

display(Markdown(query_ui_link(HIERARCHY_QUERY, "Open Corpus Hierarchy query in Wikibase Query UI")))
print("\n--- SPARQL (copy into Query UI, then switch display to 'Graph') ---\n")
print(HIERARCHY_QUERY)

Open Corpus Hierarchy query in Wikibase Query UI


--- SPARQL (copy into Query UI, then switch display to 'Graph') ---


PREFIX wd:   <https://prod-climatekg.semanticclimate.org/entity/>
PREFIX wdt:  <https://prod-climatekg.semanticclimate.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?item ?itemLabel ?linkTo ?linkToLabel WHERE {
  ?item wdt:P3 ?linkTo .

  # Only include corpus-layer items (Work / Report Series / Report / Text Division / Chapter)
  VALUES ?allowedClass { wd:Q2 wd:Q3 wd:Q4 wd:Q5 wd:Q6 }
  ?item  wdt:P1 ?allowedClass .

  OPTIONAL { ?item   rdfs:label ?itemLabel   . FILTER(LANG(?itemLabel)   = "en") }
  OPTIONAL { ?linkTo rdfs:label ?linkToLabel . FILTER(LANG(?linkToLabel) = "en") }
}
ORDER BY ?linkToLabel ?itemLabel

Show code

df_hier = run_query(HIERARCHY_QUERY)
print(f"Edges returned: {len(df_hier)}")
display(df_hier.head(20))

Edges returned: 105

1b. Python graph render (pyvis)

Renders the same hierarchy as an interactive HTML graph using pyvis.
Nodes are colour-coded by class (P1).

Show code

# Install if needed: pip install pyvis networkx
from pyvis.network import Network
import networkx as nx

# Fetch labels + class for colour coding
CLASS_QUERY = f"""
PREFIX wd:   <{WIKIBASE_URL}/entity/>
PREFIX wdt:  <{WIKIBASE_URL}/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?item ?itemLabel ?classQid WHERE {{
  VALUES ?classQid {{ wd:Q2 wd:Q3 wd:Q4 wd:Q5 wd:Q6 }}
  ?item wdt:P1 ?classQid .
  OPTIONAL {{ ?item rdfs:label ?itemLabel . FILTER(LANG(?itemLabel) = "en") }}
}}
"""

CLASS_COLOURS = {
    f"{WIKIBASE_URL}/entity/Q2": "#e74c3c",   # Work - red
    f"{WIKIBASE_URL}/entity/Q3": "#e67e22",   # Report Series - orange
    f"{WIKIBASE_URL}/entity/Q4": "#f1c40f",   # Report - yellow
    f"{WIKIBASE_URL}/entity/Q5": "#2ecc71",   # Text Division - green
    f"{WIKIBASE_URL}/entity/Q6": "#3498db",   # Chapter - blue
}
CLASS_LABELS = {
    f"{WIKIBASE_URL}/entity/Q2": "Work",
    f"{WIKIBASE_URL}/entity/Q3": "Report Series",
    f"{WIKIBASE_URL}/entity/Q4": "Report",
    f"{WIKIBASE_URL}/entity/Q5": "Text Division",
    f"{WIKIBASE_URL}/entity/Q6": "Chapter",
}

df_classes = run_query(CLASS_QUERY)
item_class  = dict(zip(df_classes["item"], df_classes["classQid"]))
item_label  = dict(zip(df_classes["item"], df_classes.get("itemLabel", df_classes["item"])))

G = nx.DiGraph()

for _, row in df_hier.iterrows():
    src = row["item"]
    tgt = row["linkTo"]
    src_label = item_label.get(src, src.split("/")[-1])
    tgt_label = item_label.get(tgt, tgt.split("/")[-1])
    G.add_node(src, label=src_label,
               color=CLASS_COLOURS.get(item_class.get(src, ""), "#95a5a6"),
               title=f"{src_label} ({CLASS_LABELS.get(item_class.get(src, ''), 'Unknown')})")
    G.add_node(tgt, label=tgt_label,
               color=CLASS_COLOURS.get(item_class.get(tgt, ""), "#95a5a6"),
               title=f"{tgt_label} ({CLASS_LABELS.get(item_class.get(tgt, ''), 'Unknown')})")
    G.add_edge(src, tgt, title="Part of")

net = Network(height="750px", width="100%", directed=True, notebook=True)
net.from_nx(G)
net.set_options("""
  var options = {
    "physics": { "hierarchicalRepulsion": { "centralGravity": 0.0 }, "solver": "hierarchicalRepulsion" },
    "layout":  { "hierarchical": { "enabled": true, "direction": "UD", "sortMethod": "directed" } },
    "edges":   { "arrows": { "to": { "enabled": true } } }
  }
""")

output_path = "../research_data/data-vis/visualization-outputs/corpus-hierarchy-graph.html"
net.save_graph(output_path)
display(HTML(f'<a href="{output_path}" target="_blank">Open corpus hierarchy graph</a>'))
net.show(output_path)

Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.

Open corpus hierarchy graph

../research_data/data-vis/visualization-outputs/corpus-hierarchy-graph.html

2. Author–Chapter Contribution Graph

Shows Authors connected to the Chapters they contributed to via P27 (contributed to chapter).
Edge direction: Author → Chapter.

⚠️ Note: the PROD instance has a known duplicate import — authors were imported twice.
The query deduplicates by P20 (ClimateKG Author ID) to avoid doubled nodes.

Show code

AUTHOR_CHAPTER_QUERY = f"""
PREFIX wd:   <{WIKIBASE_URL}/entity/>
PREFIX wdt:  <{WIKIBASE_URL}/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?item ?itemLabel ?linkTo ?linkToLabel WHERE {{

  # Deduplicate authors via P20 (ClimateKG Author ID)
  ?item wdt:P20 ?authorId .
  ?item wdt:P27 ?linkTo .
  ?linkTo wdt:P1 wd:Q6 .   # linkTo must be a Chapter

  OPTIONAL {{ ?item   rdfs:label ?itemLabel   . FILTER(LANG(?itemLabel)   = "en") }}
  OPTIONAL {{ ?linkTo rdfs:label ?linkToLabel . FILTER(LANG(?linkToLabel) = "en") }}
}}
ORDER BY ?itemLabel
"""

display(Markdown(query_ui_link(AUTHOR_CHAPTER_QUERY, "Open Author-Chapter query in Wikibase Query UI")))
print("\n--- SPARQL ---\n")
print(AUTHOR_CHAPTER_QUERY)

Open Author-Chapter query in Wikibase Query UI


--- SPARQL ---


PREFIX wd:   <https://prod-climatekg.semanticclimate.org/entity/>
PREFIX wdt:  <https://prod-climatekg.semanticclimate.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?item ?itemLabel ?linkTo ?linkToLabel WHERE {

  # Deduplicate authors via P20 (ClimateKG Author ID)
  ?item wdt:P20 ?authorId .
  ?item wdt:P27 ?linkTo .
  ?linkTo wdt:P1 wd:Q6 .   # linkTo must be a Chapter

  OPTIONAL { ?item   rdfs:label ?itemLabel   . FILTER(LANG(?itemLabel)   = "en") }
  OPTIONAL { ?linkTo rdfs:label ?linkToLabel . FILTER(LANG(?linkToLabel) = "en") }
}
ORDER BY ?itemLabel

Show code

df_auth = run_query(AUTHOR_CHAPTER_QUERY)
print(f"Author-Chapter edges: {len(df_auth)}")
print(f"Unique authors:  {df_auth['item'].nunique()}")
print(f"Unique chapters: {df_auth['linkTo'].nunique()}")
display(df_auth.head(20))

Author-Chapter edges: 1164
Unique authors:  932
Unique chapters: 75

3. Schema / Class-level Overview Graph

A meta-graph showing entity classes as nodes and the relationship types between them as edges.
Uses P1 (instance of) to infer which properties connect which class pairs.

This is the clearest overview for a report: small number of nodes, all structural relationships visible.

Show code

SCHEMA_QUERY = f"""
PREFIX wd:   <{WIKIBASE_URL}/entity/>
PREFIX wdt:  <{WIKIBASE_URL}/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?sourceClass ?sourceClassLabel ?propLabel ?targetClass ?targetClassLabel
  (COUNT(*) AS ?edgeCount)
WHERE {{
  # All WikibaseItem properties between items
  VALUES ?prop {{ wdt:P3 wdt:P4 wdt:P27 wdt:P12 }}

  ?subject  ?prop       ?object .
  ?subject  wdt:P1      ?sourceClass .
  ?object   wdt:P1      ?targetClass .

  OPTIONAL {{ ?sourceClass rdfs:label ?sourceClassLabel . FILTER(LANG(?sourceClassLabel) = "en") }}
  OPTIONAL {{ ?targetClass rdfs:label ?targetClassLabel . FILTER(LANG(?targetClassLabel) = "en") }}

  # Rewrite prop URI to a readable label
  BIND(
    IF(?prop = wdt:P3,  "Part of",
    IF(?prop = wdt:P4,  "Has parts",
    IF(?prop = wdt:P27, "contributed to chapter",
    IF(?prop = wdt:P12, "Has TAG", STR(?prop)))))
  AS ?propLabel)
}}
GROUP BY ?sourceClass ?sourceClassLabel ?propLabel ?targetClass ?targetClassLabel
ORDER BY DESC(?edgeCount)
"""

display(Markdown(query_ui_link(SCHEMA_QUERY, "Open Schema graph query in Wikibase Query UI")))
print("\n--- SPARQL ---\n")
print(SCHEMA_QUERY)

Open Schema graph query in Wikibase Query UI


--- SPARQL ---


PREFIX wd:   <https://prod-climatekg.semanticclimate.org/entity/>
PREFIX wdt:  <https://prod-climatekg.semanticclimate.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?sourceClass ?sourceClassLabel ?propLabel ?targetClass ?targetClassLabel
  (COUNT(*) AS ?edgeCount)
WHERE {
  # All WikibaseItem properties between items
  VALUES ?prop { wdt:P3 wdt:P4 wdt:P27 wdt:P12 }

  ?subject  ?prop       ?object .
  ?subject  wdt:P1      ?sourceClass .
  ?object   wdt:P1      ?targetClass .

  OPTIONAL { ?sourceClass rdfs:label ?sourceClassLabel . FILTER(LANG(?sourceClassLabel) = "en") }
  OPTIONAL { ?targetClass rdfs:label ?targetClassLabel . FILTER(LANG(?targetClassLabel) = "en") }

  # Rewrite prop URI to a readable label
  BIND(
    IF(?prop = wdt:P3,  "Part of",
    IF(?prop = wdt:P4,  "Has parts",
    IF(?prop = wdt:P27, "contributed to chapter",
    IF(?prop = wdt:P12, "Has TAG", STR(?prop)))))
  AS ?propLabel)
}
GROUP BY ?sourceClass ?sourceClassLabel ?propLabel ?targetClass ?targetClassLabel
ORDER BY DESC(?edgeCount)

Show code

df_schema = run_query(SCHEMA_QUERY)
display(df_schema)

	sourceClass	sourceClassLabel	propLabel	targetClass	edgeCount	targetClassLabel
0	https://prod-climatekg.semanticclimate.org/ent...	Acronym	Part of	https://prod-climatekg.semanticclimate.org/ent...	2993	NaN
1	https://prod-climatekg.semanticclimate.org/ent...	Category	Part of	https://prod-climatekg.semanticclimate.org/ent...	1361	NaN
2	https://prod-climatekg.semanticclimate.org/ent...	Author	contributed to chapter	https://prod-climatekg.semanticclimate.org/ent...	1164	Chapter
3	https://prod-climatekg.semanticclimate.org/ent...	Chapter	Has TAG	https://prod-climatekg.semanticclimate.org/ent...	264	Category
4	https://prod-climatekg.semanticclimate.org/ent...	Chapter	Part of	https://prod-climatekg.semanticclimate.org/ent...	75	NaN
5	https://prod-climatekg.semanticclimate.org/ent...	Author	contributed to chapter	https://prod-climatekg.semanticclimate.org/ent...	19	NaN
6	https://prod-climatekg.semanticclimate.org/ent...	Chapter	Part of	https://prod-climatekg.semanticclimate.org/ent...	14	NaN
7	https://prod-climatekg.semanticclimate.org/ent...	NaN	Has TAG	https://prod-climatekg.semanticclimate.org/ent...	13	Category
8	https://prod-climatekg.semanticclimate.org/ent...	NaN	Part of	https://prod-climatekg.semanticclimate.org/ent...	10	NaN
9	https://prod-climatekg.semanticclimate.org/ent...	NaN	Part of	https://prod-climatekg.semanticclimate.org/ent...	7	NaN
10	https://prod-climatekg.semanticclimate.org/ent...	NaN	Has TAG	https://prod-climatekg.semanticclimate.org/ent...	3	Category
11	https://prod-climatekg.semanticclimate.org/ent...	Chapter	Part of	https://prod-climatekg.semanticclimate.org/ent...	1	Chapter
12	https://prod-climatekg.semanticclimate.org/ent...	NaN	Part of	https://prod-climatekg.semanticclimate.org/ent...	1	Chapter
13	https://prod-climatekg.semanticclimate.org/ent...	NaN	Part of	https://prod-climatekg.semanticclimate.org/ent...	1	NaN

Show code

# Render schema graph with matplotlib + networkx
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

SCHEMA_COLOURS = {
    "Work":            "#e74c3c",
    "Report Series":   "#e67e22",
    "Report":          "#f1c40f",
    "Text Division":   "#2ecc71",
    "Chapter":         "#3498db",
    "Author":          "#9b59b6",
    "Category":        "#1abc9c",   # Glossary Term
    "Acronym":         "#e84393",
}

G_schema = nx.DiGraph()

for _, row in df_schema.iterrows():
    src = row.get("sourceClassLabel", row["sourceClass"].split("/")[-1])
    tgt = row.get("targetClassLabel", row["targetClass"].split("/")[-1])
    edge_label = row.get("propLabel", "?")
    count = int(row.get("edgeCount", 0))

    G_schema.add_node(src, color=SCHEMA_COLOURS.get(src, "#95a5a6"))
    G_schema.add_node(tgt, color=SCHEMA_COLOURS.get(tgt, "#95a5a6"))
    if G_schema.has_edge(src, tgt):
        G_schema[src][tgt]["label"] += f"\n{edge_label}"
        G_schema[src][tgt]["count"] += count
    else:
        G_schema.add_edge(src, tgt, label=edge_label, count=count)

pos = nx.spring_layout(G_schema, seed=42, k=3)
node_colors = [G_schema.nodes[n].get("color", "#95a5a6") for n in G_schema.nodes()]
edge_labels = {(u, v): d["label"] for u, v, d in G_schema.edges(data=True)}

fig, ax = plt.subplots(figsize=(14, 9))
nx.draw_networkx_nodes(G_schema, pos, node_color=node_colors, node_size=2200, ax=ax, alpha=0.9)
nx.draw_networkx_labels(G_schema, pos, font_size=9, font_weight="bold", ax=ax)
nx.draw_networkx_edges(G_schema, pos, arrows=True, arrowstyle="->",
                       arrowsize=20, edge_color="#555",
                       connectionstyle="arc3,rad=0.1", ax=ax)
nx.draw_networkx_edge_labels(G_schema, pos, edge_labels=edge_labels,
                             font_size=7, font_color="#333", ax=ax)

legend_patches = [mpatches.Patch(color=c, label=l) for l, c in SCHEMA_COLOURS.items()]
ax.legend(handles=legend_patches, loc="upper left", fontsize=8, title="Entity Class")
ax.set_title("ClimateKG Schema Graph — entity classes and relationship types", fontsize=13)
ax.axis("off")

out_path = "../research_data/data-vis/visualization-outputs/schema-graph.png"
plt.tight_layout()
plt.savefig(out_path, dpi=150)
plt.show()
print(f"Saved: {out_path}")

Saved: ../research_data/data-vis/visualization-outputs/schema-graph.png

4. Full Knowledge Graph — All Item-to-Item Triples

Queries every triple where both subject and object are Wikibase items.
Includes P3, P4, P12, P27.

⚠️ This returns thousands of rows. Use LIMIT for the Query UI graph — the UI renders up to ~500 nodes comfortably.

Show code

FULL_GRAPH_QUERY = f"""
PREFIX wd:   <{WIKIBASE_URL}/entity/>
PREFIX wdt:  <{WIKIBASE_URL}/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

# ------------------------------------------------------------------
# Full knowledge graph: all item-to-item relationships.
# Switch display to 'Graph' in the Wikibase Query UI.
# Adjust LIMIT for performance; 500 is comfortable in the browser.
# ------------------------------------------------------------------
SELECT DISTINCT ?item ?itemLabel ?linkTo ?linkToLabel WHERE {{

  VALUES ?prop {{ wdt:P3 wdt:P4 wdt:P12 wdt:P27 }}
  ?item  ?prop   ?linkTo .

  # Restrict to known entity classes to exclude stray items
  VALUES ?srcClass {{ wd:Q2 wd:Q3 wd:Q4 wd:Q5 wd:Q6 wd:Q2087 wd:Q3998 wd:Q1 }}
  ?item  wdt:P1  ?srcClass .

  # Deduplicate authors by P20
  OPTIONAL {{ ?item wdt:P20 ?authorId }}

  OPTIONAL {{ ?item   rdfs:label ?itemLabel   . FILTER(LANG(?itemLabel)   = "en") }}
  OPTIONAL {{ ?linkTo rdfs:label ?linkToLabel . FILTER(LANG(?linkToLabel) = "en") }}
}}
LIMIT 500
"""

display(Markdown(query_ui_link(FULL_GRAPH_QUERY, "Open Full Graph query in Wikibase Query UI (500-edge sample)")))
print("\n--- SPARQL ---\n")
print(FULL_GRAPH_QUERY)

Open Full Graph query in Wikibase Query UI (500-edge sample)


--- SPARQL ---


PREFIX wd:   <https://prod-climatekg.semanticclimate.org/entity/>
PREFIX wdt:  <https://prod-climatekg.semanticclimate.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

# ------------------------------------------------------------------
# Full knowledge graph: all item-to-item relationships.
# Switch display to 'Graph' in the Wikibase Query UI.
# Adjust LIMIT for performance; 500 is comfortable in the browser.
# ------------------------------------------------------------------
SELECT DISTINCT ?item ?itemLabel ?linkTo ?linkToLabel WHERE {

  VALUES ?prop { wdt:P3 wdt:P4 wdt:P12 wdt:P27 }
  ?item  ?prop   ?linkTo .

  # Restrict to known entity classes to exclude stray items
  VALUES ?srcClass { wd:Q2 wd:Q3 wd:Q4 wd:Q5 wd:Q6 wd:Q2087 wd:Q3998 wd:Q1 }
  ?item  wdt:P1  ?srcClass .

  # Deduplicate authors by P20
  OPTIONAL { ?item wdt:P20 ?authorId }

  OPTIONAL { ?item   rdfs:label ?itemLabel   . FILTER(LANG(?itemLabel)   = "en") }
  OPTIONAL { ?linkTo rdfs:label ?linkToLabel . FILTER(LANG(?linkToLabel) = "en") }
}
LIMIT 500

5. Glossary Terms linked to Reports

Shows which Glossary Terms (Q1) are associated with which Reports (Q4) via P3 (Part of / series_ref).

Show code

GLOSSARY_QUERY = f"""
PREFIX wd:   <{WIKIBASE_URL}/entity/>
PREFIX wdt:  <{WIKIBASE_URL}/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?item ?itemLabel ?linkTo ?linkToLabel WHERE {{
  ?item  wdt:P1 wd:Q1 .      # Glossary Term
  ?item  wdt:P3 ?linkTo .   # Part of -> Report
  ?linkTo wdt:P1 wd:Q4 .      # target must be a Report

  OPTIONAL {{ ?item   rdfs:label ?itemLabel   . FILTER(LANG(?itemLabel)   = "en") }}
  OPTIONAL {{ ?linkTo rdfs:label ?linkToLabel . FILTER(LANG(?linkToLabel) = "en") }}
}}
ORDER BY ?linkToLabel ?itemLabel
"""

display(Markdown(query_ui_link(GLOSSARY_QUERY, "Open Glossary-Report graph in Wikibase Query UI")))
print("\n--- SPARQL ---\n")
print(GLOSSARY_QUERY)

Open Glossary-Report graph in Wikibase Query UI


--- SPARQL ---


PREFIX wd:   <https://prod-climatekg.semanticclimate.org/entity/>
PREFIX wdt:  <https://prod-climatekg.semanticclimate.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?item ?itemLabel ?linkTo ?linkToLabel WHERE {
  ?item  wdt:P1 wd:Q1 .      # Glossary Term
  ?item  wdt:P3 ?linkTo .   # Part of -> Report
  ?linkTo wdt:P1 wd:Q4 .      # target must be a Report

  OPTIONAL { ?item   rdfs:label ?itemLabel   . FILTER(LANG(?itemLabel)   = "en") }
  OPTIONAL { ?linkTo rdfs:label ?linkToLabel . FILTER(LANG(?linkToLabel) = "en") }
}
ORDER BY ?linkToLabel ?itemLabel

Show code

df_gloss = run_query(GLOSSARY_QUERY)
print(f"Glossary term-Report edges: {len(df_gloss)}")
print(f"Unique terms:   {df_gloss['item'].nunique() if not df_gloss.empty else 0}")
print(f"Unique reports: {df_gloss['linkTo'].nunique() if not df_gloss.empty else 0}")
display(df_gloss.head(20))

Glossary term-Report edges: 1361
Unique terms:   913
Unique reports: 4

Query UI Links Summary

All queries ready to open directly in the Wikibase Query UI.
After opening, click the display mode button (bottom-left of the results panel) and select Graph.

Show code

links_md = "| Graph | Query UI link |\n|---|---|\n"
entries = [
    ("1. Corpus hierarchy (Part of)",        HIERARCHY_QUERY),
    ("2. Author-Chapter contributions",       AUTHOR_CHAPTER_QUERY),
    ("3. Schema / class-level overview",      SCHEMA_QUERY),
    ("4. Full KG (500-edge sample)",          FULL_GRAPH_QUERY),
    ("5. Glossary Terms linked to Reports",   GLOSSARY_QUERY),
]
for label, q in entries:
    url = QUERY_UI_BASE + urllib.parse.quote(q, safe='')
    links_md += f"| {label} | [Open]({url}) |\n"

display(Markdown(links_md))

Graph	Query UI link
1. Corpus hierarchy (Part of)	Open
2. Author-Chapter contributions	Open
3. Schema / class-level overview	Open
4. Full KG (500-edge sample)	Open
5. Glossary Terms linked to Reports	Open

	item	itemLabel	linkTo	linkToLabel
0	https://prod-climatekg.semanticclimate.org/ent...	Atlas	https://prod-climatekg.semanticclimate.org/ent...	Atlas
1	https://prod-climatekg.semanticclimate.org/ent...	Accelerating the Transition in the Context of ...	https://prod-climatekg.semanticclimate.org/ent...	Chapters
2	https://prod-climatekg.semanticclimate.org/ent...	Africa	https://prod-climatekg.semanticclimate.org/ent...	Chapters
3	https://prod-climatekg.semanticclimate.org/ent...	Agriculture, Forestry and Other Land Uses (AFOLU)	https://prod-climatekg.semanticclimate.org/ent...	Chapters
4	https://prod-climatekg.semanticclimate.org/ent...	Asia	https://prod-climatekg.semanticclimate.org/ent...	Chapters
5	https://prod-climatekg.semanticclimate.org/ent...	Australasia	https://prod-climatekg.semanticclimate.org/ent...	Chapters
6	https://prod-climatekg.semanticclimate.org/ent...	Buildings	https://prod-climatekg.semanticclimate.org/ent...	Chapters
7	https://prod-climatekg.semanticclimate.org/ent...	Central and South America	https://prod-climatekg.semanticclimate.org/ent...	Chapters
8	https://prod-climatekg.semanticclimate.org/ent...	Changing Ocean, Marine Ecosystems, and Depende...	https://prod-climatekg.semanticclimate.org/ent...	Chapters
9	https://prod-climatekg.semanticclimate.org/ent...	Changing State of the Climate System	https://prod-climatekg.semanticclimate.org/ent...	Chapters
10	https://prod-climatekg.semanticclimate.org/ent...	Cities, Settlements and Key Infrastructure	https://prod-climatekg.semanticclimate.org/ent...	Chapters
11	https://prod-climatekg.semanticclimate.org/ent...	Climate Change Information for Regional Impact...	https://prod-climatekg.semanticclimate.org/ent...	Chapters
12	https://prod-climatekg.semanticclimate.org/ent...	Climate Resilient Development Pathways	https://prod-climatekg.semanticclimate.org/ent...	Chapters
13	https://prod-climatekg.semanticclimate.org/ent...	Cross-sectoral Perspectives	https://prod-climatekg.semanticclimate.org/ent...	Chapters
14	https://prod-climatekg.semanticclimate.org/ent...	Decision-Making Options for Managing Risk	https://prod-climatekg.semanticclimate.org/ent...	Chapters
15	https://prod-climatekg.semanticclimate.org/ent...	Demand, Services and Social Aspects of Mitigation	https://prod-climatekg.semanticclimate.org/ent...	Chapters
16	https://prod-climatekg.semanticclimate.org/ent...	Desertification	https://prod-climatekg.semanticclimate.org/ent...	Chapters
17	https://prod-climatekg.semanticclimate.org/ent...	Emissions Trends and Drivers	https://prod-climatekg.semanticclimate.org/ent...	Chapters
18	https://prod-climatekg.semanticclimate.org/ent...	Energy Systems	https://prod-climatekg.semanticclimate.org/ent...	Chapters
19	https://prod-climatekg.semanticclimate.org/ent...	Europe	https://prod-climatekg.semanticclimate.org/ent...	Chapters

	item	itemLabel	linkTo	linkToLabel
0	https://prod-climatekg.semanticclimate.org/ent...	Adam Mohammed Sebbit	https://prod-climatekg.semanticclimate.org/ent...	Mitigation Pathways Compatible with Long-term ...
1	https://prod-climatekg.semanticclimate.org/ent...	Adelle Thomas	https://prod-climatekg.semanticclimate.org/ent...	Impacts of 1.5°C Global Warming on Natural and...
2	https://prod-climatekg.semanticclimate.org/ent...	Adelle Thomas	https://prod-climatekg.semanticclimate.org/ent...	Key Risks across Sectors and Regions
3	https://prod-climatekg.semanticclimate.org/ent...	Adelle Thomas	https://prod-climatekg.semanticclimate.org/ent...	Cities and Settlements by the Sea
4	https://prod-climatekg.semanticclimate.org/ent...	Aditi Mukherji	https://prod-climatekg.semanticclimate.org/ent...	High Mountain Areas
5	https://prod-climatekg.semanticclimate.org/ent...	Aditi Mukherji	https://prod-climatekg.semanticclimate.org/ent...	Water
6	https://prod-climatekg.semanticclimate.org/ent...	Aditi Mukherji	https://prod-climatekg.semanticclimate.org/ent...	Summary for Policymakers
7	https://prod-climatekg.semanticclimate.org/ent...	Aditi Mukherji	https://prod-climatekg.semanticclimate.org/ent...	Longer Report
8	https://prod-climatekg.semanticclimate.org/ent...	Adolf Acquaye	https://prod-climatekg.semanticclimate.org/ent...	Industry
9	https://prod-climatekg.semanticclimate.org/ent...	Adrian Leip	https://prod-climatekg.semanticclimate.org/ent...	Cross-sectoral Perspectives
10	https://prod-climatekg.semanticclimate.org/ent...	Adrian Spence	https://prod-climatekg.semanticclimate.org/ent...	Framing and context
11	https://prod-climatekg.semanticclimate.org/ent...	Adugna Gemeda	https://prod-climatekg.semanticclimate.org/ent...	Africa
12	https://prod-climatekg.semanticclimate.org/ent...	Agus Pratama Sari	https://prod-climatekg.semanticclimate.org/ent...	International Cooperation
13	https://prod-climatekg.semanticclimate.org/ent...	Aidan Farrell	https://prod-climatekg.semanticclimate.org/ent...	Food, Fibre and Other Ecosystem Products
14	https://prod-climatekg.semanticclimate.org/ent...	Aidan Farrell	https://prod-climatekg.semanticclimate.org/ent...	Tropical Forests
15	https://prod-climatekg.semanticclimate.org/ent...	Aimée Slangen	https://prod-climatekg.semanticclimate.org/ent...	Summary for Policymakers
16	https://prod-climatekg.semanticclimate.org/ent...	Aimée Slangen	https://prod-climatekg.semanticclimate.org/ent...	Longer Report
17	https://prod-climatekg.semanticclimate.org/ent...	Aimée Slangen	https://prod-climatekg.semanticclimate.org/ent...	Ocean, Cryosphere and Sea Level Change
18	https://prod-climatekg.semanticclimate.org/ent...	Ajay Kumar Singh	https://prod-climatekg.semanticclimate.org/ent...	Energy Systems
19	https://prod-climatekg.semanticclimate.org/ent...	Akio Kitoh	https://prod-climatekg.semanticclimate.org/ent...	Asia

	item	itemLabel	linkTo	linkToLabel
0	https://prod-climatekg.semanticclimate.org/ent...	1.5°C warmer worlds	https://prod-climatekg.semanticclimate.org/ent...	Special Report: Global Warming of 1.5°C
1	https://prod-climatekg.semanticclimate.org/ent...	1.5°C pathway	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
2	https://prod-climatekg.semanticclimate.org/ent...	13C	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
3	https://prod-climatekg.semanticclimate.org/ent...	14C	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
4	https://prod-climatekg.semanticclimate.org/ent...	Ablation	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
5	https://prod-climatekg.semanticclimate.org/ent...	Abrupt change	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
6	https://prod-climatekg.semanticclimate.org/ent...	Abrupt climate change	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
7	https://prod-climatekg.semanticclimate.org/ent...	Accumulation	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
8	https://prod-climatekg.semanticclimate.org/ent...	Active layer	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
9	https://prod-climatekg.semanticclimate.org/ent...	Adaptation	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
10	https://prod-climatekg.semanticclimate.org/ent...	Adaptation options	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
11	https://prod-climatekg.semanticclimate.org/ent...	Adaptive capacity	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
12	https://prod-climatekg.semanticclimate.org/ent...	Added value	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
13	https://prod-climatekg.semanticclimate.org/ent...	Adjustments	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
14	https://prod-climatekg.semanticclimate.org/ent...	Advection	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
15	https://prod-climatekg.semanticclimate.org/ent...	Aerosol	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
16	https://prod-climatekg.semanticclimate.org/ent...	Aerosol effective radiative forcing	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
17	https://prod-climatekg.semanticclimate.org/ent...	Aerosol optical depth	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
18	https://prod-climatekg.semanticclimate.org/ent...	Aerosol–cloud interaction	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...
19	https://prod-climatekg.semanticclimate.org/ent...	Aerosol–radiation interaction	https://prod-climatekg.semanticclimate.org/ent...	Working Group I: Climate Change 2021 – The Phy...