Why the Data Train Needs Semantic Rails -- The Case of Linked Scientometrics (APE 2015)

1. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Why the Data Train Needs Semantic Rails
The Case of Linked Scientometrics
Krzysztof Janowicz
STKO Lab, University of California, Santa Barbara, USA
APE 2015, Berlin, Germany
Why the Data Train Needs Semantic Rails K. Janowicz

2. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
How We Think About Data
Big Data As The New Natural Resource
http://www.ibmbigdatahub.com/infographic/big-data-new-natural-resource
Why the Data Train Needs Semantic Rails K. Janowicz

3. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
How We Think About Data
Big Data As The New Natural Resource
http://www.ibmbigdatahub.com/infographic/big-data-new-natural-resource
A suitable analogy?
Natural, i.e., not man-made
Exhaustible, finite quantity
Renewable, replenishable
Consumed, altered
Building block
Why the Data Train Needs Semantic Rails K. Janowicz

4. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
How We Think About Data
Big Data As The New Natural Resource
http://www.ibmbigdatahub.com/infographic/big-data-new-natural-resource
A suitable analogy?
Natural, i.e., not man-made
Exhaustible, finite quantity
Renewable, replenishable
Consumed, altered
Building block
If we don’t even understand what data are, how should we make sense out of them?
Why the Data Train Needs Semantic Rails K. Janowicz

5. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
How We Think About Data
Intoday’s discussion about data analytics,
we take data discovery, sensemaking,
and interoperability as a given.
Why the Data Train Needs Semantic Rails K. Janowicz

6. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Retrieval
The Data Retrieval Problem Is Real
Even the major data hubs such as Data.gov still rely on keyword-based search
and have unreliable, incomplete, and missing metadata. For this type of
retrieval problems, even ’a little semantics goes a long way’ (Hendler 1997).
Why the Data Train Needs Semantic Rails K. Janowicz

7. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Sensemaking
Sensemaking is Difficult – Fitness for Puspose is Key
There is no shortage of data, but
finding data that is fit for a certain
purpose is difficult.
Data as statements not as truth.
Heterogeneity is caused by cultural
differences, progress in science,
viewpoints, granularity, ...
semantics does not come for free.
Lack of provenance information
Sensemaking requires more
powerful semantic technologies and
ontologies (compared to IR).
Why the Data Train Needs Semantic Rails K. Janowicz

8. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Interoperability
Meaningful Analysis and Synthesis is Difficult
Ensuring that data is analyzed and
combined in a meaningful way is far
from trivial.
What if the information on how to
use the data would come together
with these data?
Focus on smart data instead of
(merely on) smart applications.
The purpose of ontologies is not to
agree on the meaning of terms but to
make the data provider’s intended
meaning explicit.
Why the Data Train Needs Semantic Rails K. Janowicz

9. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Semantics-Enabled Linked-Data-Driven Scientometrics
Howdoes this relate to scientometrics?
Why the Data Train Needs Semantic Rails K. Janowicz

10. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Semantics-Enabled Linked-Data-Driven Scientometrics
Semantics-Enabled Linked-Data-Driven Scientometrics
Why the Data Train Needs Semantic Rails K. Janowicz

11. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Semantics-Enabled Linked-Data-Driven Scientometrics
Value Proposition
Why do we use Semantic Web and Linked Data for Scientometrics
Federated queries over multiple data sources
Unique global identifiers easy conflation and deduplication
Transparent data model; reduces the need for guessing
No data silos, no API restrictions
Many pre-defined lightweight vocabularies (ontologies)
Smart data reduces the need for smart applications
Machine reasoning support
So do we still need a deeper knowledge representation beyond
surface semantics?
Why the Data Train Needs Semantic Rails K. Janowicz

12. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Web@25 Installation: Timeline
Keyword frequency for Semantic Web; WWW conference series (1994-2013)
Why the Data Train Needs Semantic Rails K. Janowicz

13. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Web@25 Installation: Timeline
Keyword frequency for Linked Data; WWW conference series (1994-2013)
Why the Data Train Needs Semantic Rails K. Janowicz

14. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
An Interesting Question
An Interesting Question
Given the keyword timeline, is the Semantic Web as a research field
disappearing, diversifying, radiating, ...?
Why the Data Train Needs Semantic Rails K. Janowicz

15. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
An Interesting Question
Letthe data speak for themselves
Why the Data Train Needs Semantic Rails K. Janowicz

16. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Community detection
Colors: community membership, node size: frequency, line width: co-occurrence strength
Why the Data Train Needs Semantic Rails K. Janowicz

17. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Community detection
Colors: community membership, node size: frequency, line width: co-occurrence strength
Why the Data Train Needs Semantic Rails K. Janowicz

18. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Web@25 Installation: Self-organizing Map
Landscape analogy: counties, mountains, and valleys
Why the Data Train Needs Semantic Rails K. Janowicz

19. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Web@25 Installation: Self-organizing Map
Landscape analogy: counties, mountains, and valleys
Why the Data Train Needs Semantic Rails K. Janowicz

20. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Web@25 Installation: Mapping
Location of top institutions that published on Semantic Web between 2009-2013.
Why the Data Train Needs Semantic Rails K. Janowicz

21. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Web@25 Installation: Mapping
Similar pattern for Linked Data keyword between 2009-2013.
Why the Data Train Needs Semantic Rails K. Janowicz

22. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
Web@25
Web@25 Installation: Mapping
Dissimilar pattern for Search Engine keyword between 2009-2013.
Why the Data Train Needs Semantic Rails K. Janowicz

23. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
A Tale of Three Papers
Three Papers That Shaped the Semantic Web
Citations peaked 2009 for the Ontology and Semantic Web papers.
More interestingly, why would you still cite these papers today?
Why the Data Train Needs Semantic Rails K. Janowicz

24. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
A Tale of Three Papers
Three Papers That Shaped the Semantic Web
Top keywords: {Ontology, SW},{Semantic Web, Ontology}, {Linked Data, Semantic Web, Ontology}
Why the Data Train Needs Semantic Rails K. Janowicz

25. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
A Tale of Three Papers
Three Papers That Shaped the Semantic Web
If a paper makes impact beyond its own home community, we should see an
increase in keyword variability (entropy).
Why the Data Train Needs Semantic Rails K. Janowicz

26. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
And Now?
Sois the Semantic Web
disapearing, diversifying, radiating,...?
Why the Data Train Needs Semantic Rails K. Janowicz

27. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
And Now?
Sois the Semantic Web
disapearing, diversifying, radiating,...?
No amount of data analytics is going to answer such questions before
we precisely define and communicate what we mean by those terms.
Why the Data Train Needs Semantic Rails K. Janowicz

28. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
The Semantic Cube
The Semantic Cube
Inductive and deductive techniques should be combined instead of
competing over the prerogative of interpretation
Why the Data Train Needs Semantic Rails K. Janowicz

29. A Misunderstanding Semantic Web Value Proposition Scientometrics Final Notes
The Semantic Web Journal
The Semantic Web Journal
Open:
Submitted manuscripts and
reviews are publicly available
Transparent:
Editor and reviewer names are
published together with the
paper. Editorial decisions and
paper histories are available
Volunteered:
Visitors can submit open
comments thereby contributing to
the review process
Why the Data Train Needs Semantic Rails K. Janowicz