Knowledge engineering remains relevant for developing knowledge-based systems and representing knowledge on the semantic web and in knowledge graphs. It also has applications in data science for understanding the relationships between data, models, and techniques. Recent work has applied knowledge engineering to explain data patterns, propagate data policies, and make technological artifacts more accessible to non-experts. The field can help scale and integrate tools for knowledge curation, explanation, and knowledge-driven data access and interpretation.

1. Is knowledge engineering
still relevant?
Mathieu d’Aquin - @mdaquin
mathieu.daquin@insight-centre.org
Summary of the talk: yeah, probably...

2. A quick something about me
2002-2006
2006-2017
2017-now
Knowledge representation,
case-based reasoning,
oncology
Ontology engineering,
semantic web, linked data,
smart cities, education,
privacy
Data science, data analytics,
data and knowledge
engineering, data ethics,
knowledge graphs

3. knowledge engineering
?

4. Knowledge-based systems
Knowledge-based
system
Input
Question
Problem
Situation
Answer
Inference
Solution
Decision
Knowledge
Base

5. Knowledge engineering
Knowledge-based
system
Input
Question
Problem
Situation
Answer
Inference
Solution
Decision
Knowledge
Base
Design, representation,
implementation,
manipulation, curation
and processing of this

6. Example (oncology)
M. d’Aquin et al., Knowledge editing and
maintenance tools for a semantic portal in
oncology, International journal of
human-computer studies 62 (5), 2005

7. But that’s old stuff, right?
Knowledge
base
Reasoning
Oncology
expert
Knowledge
engineer
Patient
data
Treatment
Manual effort
Classification
Machine
learning model
Training
Lots of data about
patients and treatments
Patient
data
Treatment

8. So what else did we use knowledge engineering for?
Making knowledge available and machine
processable on the Web, i.e. the Semantic Web
Ontologies: Knowledge representation with
concepts and relations.
Web ontologies: Ontologies that live well on
the web.
Linked data: Data as a graph of web entities,
and where the elements (nodes and edges) are
defined by web ontologies.
Knowledge graphs: See linked data ;)

9. Shameless plug
The semantic web
Network of
knowledge artefact
Linked Data
Network of data
artefact
The Web
Network of
documents
The Internet
Network of machines

10. Example
Annotating correspondances from early women philosophers
(PhD of Ioana Kyvernitou)

11. Example
Annotating correspondances from early women philosophers
(PhD of Ioana Kyvernitou)

12. Knowledge engineering for the meta-aspects of Data Science
Hypo. /
Question
Plan Collect
data
Analyse
data
Extract
results
Exploit
results
Data Models
New
info
What-
ever
was the
goal

13. Anything new?
Knowledge engineering for the meta-aspects of Data Science
Hypo. /
Question
Plan Collect
data
Analyse
data
Extract
results
Exploit
results
Data Models
New
info
What-
ever
was the
goalDataset
License
Regulation
Source
Dataset
Characteristics
Data
Science
Task
Technique
Model
Model
Parameters
...
associated with
obtained from with
derived from
used for
implemented by
using
produced
version of
produced

14. Propagating data policies
Propagation of policies in rich data flows
E Daga, M d'Aquin, A Gangemi, E Motta,
International Conference on Knowledge
Capture, K-CAP 2015

15. Explaining data patterns
Dedalo: Looking for clusters explanations in
a labyrinth of linked data, I Tiddi, M
d’Aquin, E Motta European Semantic Web
Conference, 2014
An ontology design pattern to define
explanations, I Tiddi, M d'Aquin, E Motta
Proceedings of the 8th International
Conference on Knowledge Capture, 2015

16. Understanding the link between data and what is done with it
(PhD Mohamed Adel)

17. Understanding the link between data and what is done with it
(PhD Mohamed Adel)

18. Making technological artefacts more non-expert friendly
An ontology-based approach to improve the
accessibility of ROS-based robotic systems
I Tiddi, E Bastianelli, G Bardaro, M d'Aquin, E
Motta, Knowledge Capture Conference, 2017
Extracting robot’s capabilities through automatically annotating components of
ROS (the Robot Operating System) with an ontology

19. Making technological artefacts more non-expert friendly
An ontology-based approach to improve the
accessibility of ROS-based robotic systems
I Tiddi, E Bastianelli, G Bardaro, M d'Aquin, E
Motta, Knowledge Capture Conference, 2017

20. What about making research data more
non-data expert friendly?

21. Ontologies to represent the base capabilities of datasets

23. Mapping data access to basic ontology operations
[{ "datasetId": "tempo",
"load":
"pd.read_csv('/home/mdaquin/data/TempoData/Beethoven_Op57_Tempi.csv')",
"types": {
"performance": {
"list": "[{'music': 'Bethoveen Op57', 'name': s.lower()} for s in
list(data.columns.values)[:-2]]",
"attributes": "{'music': 'music', 'name': 'value', bars:
'[peformedBar]'}",
"values": "if attribute=='bars':n result=[]n col=0n for
x,coln in enumerate(list(data.columns.values)[:-2]):n if
coln.lower()==obj['name']:n col=colnn for i in range(0,
len(data[col])):n result.append({'bar': {'music': 'Bethoveen Op57',
'number': i}, 'performance': {'music': 'Bethoveen Op57', 'name':
obj['name']}})"
},
"performedBar": {
"list": "[{'bar': {'music': 'Bethoveen Op57', 'number':m},
'performance': {'music': 'Bethoveen Op57', 'name': n.lower()}} for m in
list(range(0,data.shape[0])) for n in list(data.columns.values)[:-2]]",
"attributes": "{'bar': 'bar','performance': 'performance','tempo':
'value'}",
"values": "result=['unknown']nif attribute=='bar':n result =
[obj['bar']]nif attribute=='performance':n
result=[obj['performance']]nif attribute=='tempo':n col=0n for x,coln

24. Ontology-based data access in scratch

25. Ontology-based data access in scratch

26. Ontology-based data access in scratch

27. Ontology-based data access in scratch

28. Ontology-based data access in scratch

29. Conclusion
Knowledge is, more than ever, a necessary, valuable asset, and
some amounts of knowledge engineering can (and is) useful in
event the most top-down, data-centric of processes.
Need to not only scale, but also make more accessible, and more
integrated the tools to enable knowledge curation,
knowledge-based explanations/interpretation, and
knowledge-driven data access, integration and interpretation.
One size does not fit all: querying web
polystores, Y Khan, A Zimmermann, A Jha, V
Gadepally, M D’Aquin, R Sahay
Ieee Access 7, 2019
Towards an ethics by design methodology
for AI research projects, M d'Aquin, P
Troullinou, NE O'Connor, A Cullen, G Faller,
L Holden AAAI/ACM Conference on AI,
Ethics, and Society, 2018
Crowdsourcing Linked Data on listening
experiences through reuse and
enhancement of library data, A Adamou et
al. International Journal on Digital
Libraries 20 (1), 2019