ClimateKG SPARQL Question Bank

This page is a short starting point for a query-first notebook series for the Climate Knowledge Graph. It groups a small set of candidate questions by dataset area so each notebook stays focused and reusable.

Goal

The aim is to show how the ClimateKG graph can be queried with SPARQL and explained in notebooks. The focus is on evidence, reproducibility, and clear results.

Dataset areas and question groups

1. Corpus and report structure

What is the structure of the corpus hierarchy?
How many items are there at each level?
Which reports and chapters are present?

2. Bibliography and publication metadata

Which publications are linked to each report or chapter?
How complete is the bibliographic metadata?
Which reports are most heavily referenced?

3. Authors and contributors

How many distinct authors are represented?
Which authors contribute to more than one report or chapter?
What does author contribution look like across the reports?

4. Glossary and conceptual vocabulary

Which glossary terms are attached to a report?
How are glossary terms connected to reports and definitions?
Which terms appear across multiple reports?

5. Acronyms and terminology

Which acronyms appear most often?
Which reports or chapters use them?
Are any acronyms repeated in different contexts?

6. Cross-dataset and evidence queries

How can bibliographic, author, and chapter data be connected?
Which corpus elements link to glossary terms or acronyms?
Where are the main gaps or inconsistencies?

Initial notebook series proposal

Notebook 1: class instance counts

Purpose: count the main classes and establish a baseline.

Suggested questions: - How many items are there in each major class? - Do the hierarchy counts match the document structure?

Notebook 2: investigation of Q1 count discrepancy

Purpose: explain the main count discrepancy.

Suggested questions: - Why do the Q1 counts differ from the expected total? - Are there duplicates or missing links?

Notebook 3: glossary triple demonstration

Purpose: show a simple RDF triple example for a glossary term.

Suggested questions: - What does a glossary triple look like? - Which properties connect the term to a report?

Notebook 4: bibliography and report linking

Purpose: connect publications, reports, and chapter evidence.

Suggested questions: - Which entries link to which reports? - Which reports have the most references?

Notebook 5: authorship and contribution analysis

Purpose: measure author participation.

Suggested questions: - Which authors contribute to multiple reports or chapters? - Which report sections have the most authors?

Notebook 6: glossary and acronym crosswalks

Purpose: connect terminology and acronyms to the corpus.

Suggested questions: - Which terms are associated with which reports? - Which acronyms recur in the same contexts?

Query-first workflow

Each notebook should follow this pattern:

Define the question.
Convert it to SPARQL.
Validate the live result.
Document the result in text and tables.
Add visualisation only after the query is stable.

Current Wikibase instance

Main site: https://climatekg.tibwiki.io/
SPARQL query interface: https://climatekg.tibwiki.io/query/
Local notebook access may require the current deployment credentials.

Next implementation step

The next step is to update the notebooks for the current instance and validate a small first set of queries for the corpus and bibliography domains.

# ClimateKG SPARQL Question Bank This page is a short starting point for a query-first notebook series for the Climate Knowledge Graph. It groups a small set of candidate questions by dataset area so each notebook stays focused and reusable. ## Goal The aim is to show how the ClimateKG graph can be queried with SPARQL and explained in notebooks. The focus is on evidence, reproducibility, and clear results. ## Dataset areas and question groups ### 1. Corpus and report structure - What is the structure of the corpus hierarchy? - How many items are there at each level? - Which reports and chapters are present? ### 2. Bibliography and publication metadata - Which publications are linked to each report or chapter? - How complete is the bibliographic metadata? - Which reports are most heavily referenced? ### 3. Authors and contributors - How many distinct authors are represented? - Which authors contribute to more than one report or chapter? - What does author contribution look like across the reports? ### 4. Glossary and conceptual vocabulary - Which glossary terms are attached to a report? - How are glossary terms connected to reports and definitions? - Which terms appear across multiple reports? ### 5. Acronyms and terminology - Which acronyms appear most often? - Which reports or chapters use them? - Are any acronyms repeated in different contexts? ### 6. Cross-dataset and evidence queries - How can bibliographic, author, and chapter data be connected? - Which corpus elements link to glossary terms or acronyms? - Where are the main gaps or inconsistencies? ## Initial notebook series proposal ### Notebook 1: class instance counts Purpose: count the main classes and establish a baseline. Suggested questions: - How many items are there in each major class? - Do the hierarchy counts match the document structure? ### Notebook 2: investigation of Q1 count discrepancy Purpose: explain the main count discrepancy. Suggested questions: - Why do the Q1 counts differ from the expected total? - Are there duplicates or missing links? ### Notebook 3: glossary triple demonstration Purpose: show a simple RDF triple example for a glossary term. Suggested questions: - What does a glossary triple look like? - Which properties connect the term to a report? ### Notebook 4: bibliography and report linking Purpose: connect publications, reports, and chapter evidence. Suggested questions: - Which entries link to which reports? - Which reports have the most references? ### Notebook 5: authorship and contribution analysis Purpose: measure author participation. Suggested questions: - Which authors contribute to multiple reports or chapters? - Which report sections have the most authors? ### Notebook 6: glossary and acronym crosswalks Purpose: connect terminology and acronyms to the corpus. Suggested questions: - Which terms are associated with which reports? - Which acronyms recur in the same contexts? ## Query-first workflow Each notebook should follow this pattern: 1. Define the question. 2. Convert it to SPARQL. 3. Validate the live result. 4. Document the result in text and tables. 5. Add visualisation only after the query is stable. ## Current Wikibase instance - Main site: https://climatekg.tibwiki.io/ - SPARQL query interface: https://climatekg.tibwiki.io/query/ - Local notebook access may require the current deployment credentials. ## Next implementation step The next step is to update the notebooks for the current instance and validate a small first set of queries for the corpus and bibliography domains.