This document discusses knowledge engineering and how it sits between software engineering and artificial intelligence. It focuses on building and maintaining knowledge-based systems that capture human expertise to support decision making. The document examines challenges with involving more people in knowledge engineering processes and how automation could help. It explores options like games, online communities, and human computation to engage people while also discussing how artificial intelligence advances may assist with knowledge representation and engineering challenges.

1. Knowledge
engineering: from
people to machines
and back
Elena Simperl, @esimperl

2. Knowledge
engineering
• Building and maintaining knowledge-
based systems
• Sits between
• Software engineering: how to build and
maintain software systems; and
• AI: how to represent knowledge
computationally
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3. Knowledge-based
systems
• A form of artificial intelligence
• Capture the knowledge of human
experts computationally to support
decision making
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4. 4

5. 5
Wikidata knowledge base: talks
about entities of interest in
different languages
Wikidata information is shown in
the infobox on the right in
Wikipedia

6. 6

7. Knowledge-
based systems
• Complementary to data-based
AI e.g., machine learning
• Most AI systems use both
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8. Standard
knowledge
engineering
process
(early 2000s)
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9. Case studies,
guidance,
templates to
reuse
ontologies
effectively
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10. Predicting
costs,
understanding
the most
resource-
intensive part of
the process
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11. Two ways forward
Automation Involve more people
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12. Involve
more
people:
two
challenges
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Participation
Data quality

13. Participation:
aligning tasks
to people’s
motivations,
designing
incentives
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14. People implicitly
contribute to the
knowledge base
while going about
their regular work.
Top contributors
are rewarded.
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15. People play games.
Games data is turned
into computational
knowledge
Challenges: design, attracting and
retaining players, costs
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16. People are
part of online
communities
that build
knowledge
bases
Challenges: uneven
participation, data
quality 16

17. Improving
quality: data
provenance
Improve data provenance at scale
with machine learning and paid
crowdsourcing
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18. Improving
quality: team
composition
Editor diversity and human-bot
interactions drive quality
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19. Improving quality:
content gaps
Fixing endogenous gaps: gender
and recency gaps linked to topic
popularity, need to act on socio-
economic gaps
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20. What if
automation
were an
option?
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Human computation Artificial intelligence

21. Human computation: people
improve performance of AIs
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22. Advances in machine
learning can help with
knowledge
engineering
Challenges: trustworthiness, human-
AI interaction
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23. Thank you
Elena
Simperl
@esimperl
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