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UID:pretalx-coscup-2026-N89AEH@pretalx.coscup.org
DTSTART;TZID=CST:20260808T113000
DTEND;TZID=CST:20260808T120000
DESCRIPTION:## Description\n\nModern data is highly connected — such as s
 upply chains\, networks\, and relationships.\n\nPostgreSQL is strong for r
 elational queries\, but when we need to follow multi-step connections\, qu
 eries quickly become complex. JOINs grow\, and the SQL becomes harder to r
 ead\, maintain\, and extend.\n\nSQL/PGQ (SQL:2023) brings graph query capa
 bilities into SQL. It allows us to describe connections and paths more dir
 ectly\, without leaving PostgreSQL.\n\nSQL/PGQ is currently under developm
 ent in PostgreSQL\, and early implementations are already available for ex
 perimentation.\n\nIn this talk\, I show how queries evolve from simple joi
 ns to multi-step paths. We compare traditional SQL and SQL/PGQ\, focusing 
 on readability and how we think about the problem.\n\nUsing a small trade 
 network example\, I demonstrate how SQL/PGQ can simplify queries and help 
 us better understand connected data.\n\n## Outline\n\n- Problem: complex J
 OIN queries  \n- SQL/PGQ basics (SQL:2023)  \n- SQL vs SQL/PGQ examples  \
 n- Graph thinking in PostgreSQL  \n- Current status and how to try it  \n\
 n## Key Takeaways\n\n- JOINs become complex for multi-step queries  \n- SQ
 L/PGQ simplifies relationship queries  \n- Graph thinking improves underst
 anding of data  \n- SQL becomes more powerful\, not replaced
DTSTAMP:20260713T132516Z
LOCATION:RB102
SUMMARY:From JOINs to Graph Thinking: Practical SQL/PGQ in PostgreSQL - Koj
 i Annoura
URL:https://pretalx.coscup.org/coscup-2026/talk/N89AEH/
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BEGIN:VEVENT
UID:pretalx-coscup-2026-LCKXCL@pretalx.coscup.org
DTSTART;TZID=CST:20260809T133000
DTEND;TZID=CST:20260809T140000
DESCRIPTION:## Description\n\nUnderwater cultural heritage is fragile and o
 ften invisible — not only beneath the sea\, but buried in PDFs\, scatter
 ed archives\, and disconnected databases. While LLMs can generate summarie
 s\, they do not preserve structured\, verifiable knowledge over time. Rela
 tional tables store records\, but struggle to capture complex relationship
 s such as trade routes\, ocean currents\, artifacts\, and cross-border exc
 hange.\n\nThis talk proposes modeling underwater heritage as an open knowl
 edge graph — not as a product\, but as shared\, open infrastructure for 
 cultural memory.\n\nThese ideas align with open knowledge ecosystems such 
 as Wikidata and OpenStreetMap\, where structured\, linked data enables glo
 bal collaboration across communities and domains.\n\nRather than focusing 
 on specific tools\, the session introduces open modeling principles and li
 nked data concepts that make knowledge interoperable\, extensible\, and co
 mmunity-driven. We will also briefly demonstrate a minimal example using N
 eo4j as one practical implementation.\n\nTaiwan\, like Japan\, is shaped b
 y maritime history. What if we could structure this shared heritage as an 
 open\, cross-border knowledge graph — collaboratively maintained and glo
 bally connected?\n\n**At its core\, this talk asks:**\n\n**If knowledge mu
 st outlive tools\, models\, and vendors — where should it live?**\n\nThi
 s session is for beginners and anyone interested in open data\, linked dat
 a\, and meaningful knowledge design. All examples use open data and open t
 ools\, so you can explore and extend the approach yourself.\n\n## Outline 
 (30 minutes)\n\n1. Fragmented cultural data  \n2. Limits of PDFs\, tables\
 , and AI-generated summaries  \n3. Open knowledge graph and linked data pr
 inciples  \n4. Minimal example (Neo4j as one implementation)  \n5. Cross-b
 order and community-driven knowledge  \n\n## Key Takeaways\n\nParticipants
  will:\n\n- Understand why current approaches fail to capture complex rela
 tionships  \n- Learn how to model heritage as interconnected\, structured 
 data  \n- See a simple\, practical graph-based example  \n- Understand the
  role of open\, linked knowledge in global and cross-border collaboration
DTSTAMP:20260713T132516Z
LOCATION:TR412-1
SUMMARY:Underwater Heritage as an Open Knowledge Graph with Neo4j - Koji An
 noura
URL:https://pretalx.coscup.org/coscup-2026/talk/LCKXCL/
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BEGIN:VEVENT
UID:pretalx-coscup-2026-LDNNEY@pretalx.coscup.org
DTSTART;TZID=CST:20260809T142000
DTEND;TZID=CST:20260809T145000
DESCRIPTION:Many of us know SPY×FAMILY — a “family” where each membe
 r has a hidden identity. At first\, it looks like a simple family story\, 
 but behind it is a network of hidden relationships.\n\nWhat if we could vi
 sualize such hidden relationships in knowledge?\n\nIn the open-source worl
 d\, we have access to a huge amount of unstructured data: Wikipedia\, docu
 ments\, and web pages. These sources are easy to read\, but difficult to r
 euse\, connect\, and track as their content changes over time. Relationshi
 ps may be hidden in text\, but their importance\, context\, and changes ov
 er time are even harder to see.\n\nThe core of this session is a step-by-s
 tep demonstration of how to turn a Wikipedia page into a knowledge graph u
 sing Neo4j and the Neo4j LLM Knowledge Graph Builder. The demonstration us
 es LLMs to extract entities and relationships from the Wikipedia page\, an
 d stores the extracted knowledge as a graph that can be explored\, compare
 d\, and reused.\n\nThen we will pick two versions of the same Wikipedia pa
 ge — past and present — and compare them as graphs. This allows us to 
 see how knowledge evolves: what was added\, what changed\, and how relatio
 nships grow over time.\n\nThis 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 star
 t from open data\, build a graph\, compare versions\, and apply the same a
 pproach to their own documents or web content.\n\nThis talk is not only ab
 out building graphs. It is about exploring knowledge\, comparing it\, and 
 understanding how it grows.\n\nEverything in this session is based on open
  data and open tools. No special dataset is required — just use the data
  already around you.
DTSTAMP:20260713T132516Z
LOCATION:TR410
SUMMARY:From SPY×FAMILY to Evolving Knowledge Graphs: Tracking Wikipedia C
 hanges with Neo4j and LLMs - Koji Annoura
URL:https://pretalx.coscup.org/coscup-2026/talk/LDNNEY/
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