Shirley Bacso, Data Architect, Ingka Digital “Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.

1. Linked Metadata by Design
Presented by: Shirley Bacso
Data Architect at Ingka Digital, Netherlands

2. A few words
about me
• Data Architect at Ingka Digital, Netherlands
• Specialize in metadata
• Love problem-solving & learning
• Graph enthusiast since June 2021

3. My journey
June 2021
Oct 2021
May 2022
Implementing
Business
Glossary
Ingka Graph
Event with
Neo4j
Feb 2023
Knowledge Graphs
2023, Prof. Dr.
Harald Sack
Oct 2023
DKG MVP
NeoDash
Feb 2024
Learn graph
technology
Exam case
studies
Research metadata
management
best practices
Study organization
realities
Observe people
Hunt for metadata
Hunt for metadata
Hunt for metadata
Observe people
Study organization
realities
Hunt for metadata
Observe people
Metadata – the knowledge of our data Dec 2023

4. Rationale

5. What types of questions should be answered by
the knowledge graph?
Do all the data
columns/fields have
business names?
Are the data compliant
to (retention) rules?
What are the gaps
between the data quality
dimension goals and the
reality?
Do all the databases
connected to the
software systems?
What systems does a
certain digital domain
have?
What type of personal
data does a software
system have?
Find the most relevant
dataset
(recommendation
engine) and request
access.
Which data products are
overlapping? (Think
about the Principle of
Data as a Product)
How was the data
transformed throughout
the lifetime?
(Provenance and
operational lineage)
…

6. Which knowledge areas should be covered ?
Collectively capture
the broader knowledge
about data

7. arrows.app
What concepts and relationships should be in
the knowledge graph?

8. The Ontology Development Process
Knowledge Graphs 2023, Prof. Dr. Harald Sack, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure & Karlsruhe Institute of Technology
Ontology development in practice is an iterative process that repeats
continuously and improves the ontology.
What types of questions
should be answered by the
knowledge graph? Which knowledge areas
should be covered by the
knowledge graph? What concepts and
relationships should be in
the knowledge graph?

9. Pain points of “Metadata as a by-product” and relieves
DAMA DMBOK has pointed out:
“Metadata is often created as a
by-product of application
processing rather than as an
end product (i.e.. it is not
created with consumption in
mind). As with other forms of
data, there is a lot of work in
preparing Metadata before it
can be integrated.”

10. What is linked
metadata by
design?
• Linked metadata represents the integration
of outcomes from human collaboration. It
collectively forms the knowledge about
data, which is captured in the data
knowledge graph.
• By design emphasizes that the capture and
integration of metadata should start from
the conceptual level and continue through
the logical level, as human collaboration
begins at the design phase of data product
development.

11. Solution

12. Architecture
Various
metadata
sources
L0 L1 L2 L3
“Sources of knowledge” “Data knowledge graph”

13. What has been done

14. Column
Table
Schema
System group
Database
Digital domain
Digital unit
System
Architectural area
The current version of the
DKG has close to 1 million of
nodes and 1 million of edges
and it will continue to grow.
This is a snapshot from Neo4j
Bloom. Only 10K nodes (1%)
are displayed here.
Ingka Data Knowledge Graph (DKG)

15. NeoDash (UI)
Query
Visualize
Post changes
Ingka Data
Knowledge Graph
(DB)
https://github.com/neo4j-labs/neodash
NeoDash - Neo4j Dashboard Builder
NeoDash is an open source tool for
visualizing your Neo4j data. It lets you group
visualizations together as dashboards, and
allow for interactions between reports.
Explicit, formal and shareable
knowledge about data/ data products
stored in a graph database

16. Catalogued
Systems
Quick Insights
Intuitive Visuals
Coherent Understanding
Ø Systems are categorized
• by relations to other asset
types:
• Architectural Area
• System Group
• Digital Unit | Digital Domain
• by categorical attributes
Ø Explore details of the
system in interest

17. Catalogued
RDBs
Ø Datasources
are categorized by relations to
other asset types:
System à Digital Unità Digital Domain
Ø Explore details of the
datasource in interest and
its hierarchical assets
Column à Table à Schema à
Datasource
Quick Insights
Intuitive Visuals
Coherent Understanding

18. Consumer -
Universal
Search
Universal and
exploratory search for
various asset types, e.g.
System, Datasource…

19. Find | Inspect | Connect | Verify
Without the need to build and
maintain UIs
Link Data Concept to:
• Business Term
• Data Entities
• …
Link Data Attribute/Entity to:
• Business Term
• Code List
• Rules
• Policies
• …
Link Data Attribute to:
• Columns, Fields...
Provider –
Link Metadata

20. Current development and future

21. Column
Table
Schema
System group
Database
Digital domain
Digital unit
System
Architectural area

22. Near future potentials
• Knowledge graph completion: Link prediction with Word
Embeddings and KG Embeddings

23. Near future potentials
• Knowledge graph completion: Link prediction with Word
Embeddings and KG Embeddings
Knowledge Graphs 2023, Prof. Dr. Harald Sack, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure & Karlsruhe Institute of Technology

24. Near future potentials
• Better search: Use combined capabilities of semantic and
structural search
• Refinement enables more precise or more complete search
results.
Query String
• Enables complementing search results with additional
associated or similar information.
Cross Referencing
• Enables the determination of nearby results and results related
by content.
Fuzzy Search
• Enables visualization and navigation of the search space.
Exploratory Search via
Linked Data
• Enables complementing search results with implicitly given
information.
Reasoning
Knowledge Graphs 2023, Prof. Dr. Harald Sack, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure & Karlsruhe Institute of Technology

25. Near future potentials
• Leverage LLM + KG RAG
Tomaz Bratanic
Learn | Share | Collaborate
Ask DKG
anything about
your data

26. Near future potentials
• Build fast apps in OpenSource and InnerSource

27. Knowledge Building Community
o Terminology, ontology and modeling
o Data governance
o Data engineering
o Graph data science
o UX design and UI building
o …
• Iteration of curating and extracting knowledge
• Following the architecture of building and
consuming the knowledge graph
• By the many people, for the many people

29. Thank you!
Reach out on Shirley Bacso