This document discusses dealing with open domain data and recent examples. It begins by explaining that typical knowledge systems are closed domain, while open domain systems can answer unknown questions. It then discusses early work using the Watson ontology and Semantic Web to build open domain question answering. A core assumption was that the Semantic Web would know everything if it continued growing, which did not occur. However, recent projects like AFEL have shown the Semantic Web and DBpedia can represent data from many domains and be used for tasks like detecting topics in activity streams, explaining patterns in data, and finding biases. While applications using open domain linked data are still limited, the ability to represent diverse data in a single graph remains important.

1. Dealing with
open domain data
Mathieu d’Aquin - @mdaquin
Data Science Institute
Insight Centre for Data Analytics
NUI Galway

2. Dealing with
open domain data
Mathieu d’Aquin - @mdaquin
Data Science Institute
Insight Centre for Data Analytics
NUI Galway

3. Typical
knowledge-based
systems are not
open domain
d’Aquin et al. "Knowledge editing
and maintenance tools for a
semantic portal in oncology."
International journal of
human-computer studies 62, no.
5 (2005): 619-638.
Lieber et al.. "Modeling
adaptation of breast cancer
treatment decision protocols in
the KASIMIR project." Applied
Intelligence 28, no. 3 (2008)
d'Aquin et al., "Towards a
semantic portal for oncology
using a description logic with
fuzzy concrete domains." In
Capturing Intelligence, vol. 1, pp.
379-393. Elsevier, 2006.

4. Open domain: When we
don’t know what we are
going to be asked
Typically, question-answering or
semantic search, but also many
others.
Lopez et al. "Scaling up question-answering to linked data."
In International Conference on Knowledge Engineering and
Knowledge Management, pp. 193-210. Springer, Berlin,
Heidelberg, 2010.
d'Aquin, Mathieu, Marta Sabou, Enrico Motta, Sofia
Angeletou, Laurian Gridinoc, Vanessa Lopez, and Fouad
Zablith. "What can be done with the Semantic Web? An
Overview of Watson-based Applications." In CEUR Workshop
Proceedings, vol. 426. 2008.

5. Based on KMi Watson
Ontology search engine and semantic web gateway
d'Aquin and Motta. "Watson, more than a semantic web search engine." Semantic Web 2, no. 1 (2011): 55-63.

6. (over simplified)
Core assumption
(at the time, i.e. ~2007)
If the Semantic Web carries on growing in the same way as
it dies, it will end up knowing everything

7. That did not really happen, but...
Open domain, intelligent applications based on the
semantic web still do

8. A recent
example
The AFEL (Analytics
for Everyday Learning)
project:
http://afel-project.eu
@afelProject
d’Aquin et al. "AFEL: Towards
Measuring Online Activities
Contributions to Self-Directed
Learning.", ARTEL 2017
workshop at EC-TEL.
d'Aquin et al. "AFEL-Analytics
for Everyday Learning." In
Companion of the The Web
Conference 2018.

9. The AFEL process
AFEL Data
Platform
InputAPIs
OutputAPIs
Target
platform
AFEL
Mobile
app
AFEL
Visual
Analytics
AFEL
Rec.
Services
enriched activity
data and
indicators
enriched activity
data and
indicators
recommendations
activity data
resource
text and
metatada
resources
and
activities

10. Detecting learning scopes (i.e. topics) in activity streams
Zainab and d’Aquin, Detection of Online Learning Activity Scopes, AFEL workshop at EC-TEL 2018

11. Detecting learning scopes (i.e. topics) in activity streams
Zainab and d’Aquin, Detection of Online Learning Activity Scopes, AFEL workshop at EC-TEL 2018
DBpedia Spotlight
Abstraction throughDBpedia categories
Similar to
k-means
Based on TF.IDF of
DBpedia ent./cats

13. Revised assumption
The Semantic Web/DBpedia might not know everything,
but they might know something about almost everything

14. For something more clever: Explain patterns
Tiddi et al. "Data patterns explained with linked data." In ECML/PKDD 2015
Tiddi et al. "Dedalo: Looking for clusters explanations in a labyrinth of linked data." In ESWC 2014

15. Or finding biases in datasets
Using DBpedia as a reference dataset, found for example that:
- A dataset about places in Finland had a significant focus on Finland, and the
longitudes/latitudes in Finland (obvious)
- A dataset about (mostly UK-based) writers had a significant focus on authors of
novels and poetry, and also who had suicide as cause of death
Tiddi et al. "Quantifying the bias in data links." K-CAP 2014

16. Conclusions
The notion of what the semantic web and linked data can achieve
has evolved a lot in the last 15 years.
But, the ability for them to represent in the same space data from
many different domains is still a key aspect.
Still only few applications have exploited it, due to:
- Lack of robustness of the methods to access the data
- Lack of understanding of the benefits of linked data
- Lack of integration with with other tools (reasoning, mining,
machine learning).
Parenthesis: Linked Data - One graph or a collection of datasets?
Thank you! Contacts: @mdaquin - mathieu.daquin@insight-centre.org -
mdaquin.net