COSCUP 2026 - Conference for Open Source Coders, Users, and Promoters

Description

Modern data is highly connected — such as supply chains, networks, and relationships.

PostgreSQL is strong for relational queries, but when we need to follow multi-step connections, queries quickly become complex. JOINs grow, and the SQL becomes harder to read, maintain, and extend.

SQL/PGQ (SQL:2023) brings graph query capabilities into SQL. It allows us to describe connections and paths more directly, without leaving PostgreSQL.

SQL/PGQ is currently under development in PostgreSQL, and early implementations are already available for experimentation.

In this talk, I show how queries evolve from simple joins to multi-step paths. We compare traditional SQL and SQL/PGQ, focusing on readability and how we think about the problem.

Using a small trade network example, I demonstrate how SQL/PGQ can simplify queries and help us better understand connected data.

Outline

• Problem: complex JOIN queries
• SQL/PGQ basics (SQL:2023)
• SQL vs SQL/PGQ examples
• Graph thinking in PostgreSQL
• Current status and how to try it

Key Takeaways

• JOINs become complex for multi-step queries
• SQL/PGQ simplifies relationship queries
• Graph thinking improves understanding of data
• SQL becomes more powerful, not replaced

Description

Underwater cultural heritage is fragile and often invisible — not only beneath the sea, but buried in PDFs, scattered archives, and disconnected databases. While LLMs can generate summaries, they do not preserve structured, verifiable knowledge over time. Relational tables store records, but struggle to capture complex relationships such as trade routes, ocean currents, artifacts, and cross-border exchange.

This talk proposes modeling underwater heritage as an open knowledge graph — not as a product, but as shared, open infrastructure for cultural memory.

These ideas align with open knowledge ecosystems such as Wikidata and OpenStreetMap, where structured, linked data enables global collaboration across communities and domains.

Rather than focusing on specific tools, the session introduces open modeling principles and linked data concepts that make knowledge interoperable, extensible, and community-driven. We will also briefly demonstrate a minimal example using Neo4j as one practical implementation.

Taiwan, like Japan, is shaped by maritime history. What if we could structure this shared heritage as an open, cross-border knowledge graph — collaboratively maintained and globally connected?

At its core, this talk asks:

If knowledge must outlive tools, models, and vendors — where should it live?

This session is for beginners and anyone interested in open data, linked data, and meaningful knowledge design. All examples use open data and open tools, so you can explore and extend the approach yourself.

Outline (30 minutes)

1. Fragmented cultural data
2. Limits of PDFs, tables, and AI-generated summaries
3. Open knowledge graph and linked data principles
4. Minimal example (Neo4j as one implementation)
5. Cross-border and community-driven knowledge

Key Takeaways

Participants will:

• Understand why current approaches fail to capture complex relationships
• Learn how to model heritage as interconnected, structured data
• See a simple, practical graph-based example
• Understand the role of open, linked knowledge in global and cross-border collaboration

Many of us know SPY×FAMILY — a “family” where each member has a hidden identity. At first, it looks like a simple family story, but behind it is a network of hidden relationships.

What if we could visualize such hidden relationships in knowledge?

In the open-source world, we have access to a huge amount of unstructured data: Wikipedia, documents, and web pages. These sources are easy to read, but difficult to reuse, connect, and track as their content changes over time. Relationships may be hidden in text, but their importance, context, and changes over time are even harder to see.

The core of this session is a step-by-step demonstration of how to turn a Wikipedia page into a knowledge graph using Neo4j and the Neo4j LLM Knowledge Graph Builder. The demonstration uses LLMs to extract entities and relationships from the Wikipedia page, and stores the extracted knowledge as a graph that can be explored, compared, and reused.

Then we will pick two versions of the same Wikipedia page — past and present — and compare them as graphs. This allows us to see how knowledge evolves: what was added, what changed, and how relationships grow over time.

This session is for developers, data engineers, and open-source contributors who want to build their own knowledge graph environment. By following the steps, participants will learn how to start from open data, build a graph, compare versions, and apply the same approach to their own documents or web content.

This talk is not only about building graphs. It is about exploring knowledge, comparing it, and understanding how it grows.

Everything in this session is based on open data and open tools. No special dataset is required — just use the data already around you.