1.
Francesco Osborne1, Angelo Salatino1, Aliaksandr Birukou2,
Thiviyan Thanapalasingam1, Enrico Motta1
1
Knowledge Media Institute, The Open University, United Kingdom
2
Springer Nature, Heidelberg, Germany
ISWC 2017
Supporting Springer Nature Editors
by means of Semantic Technologies

2.
The collaboration
The Open University and Springer Nature have been
collaborating since 2015 in the development of an array of
semantically-enhanced solutions for:
2
• Automatic classification of
proceedings and other editorial
products.
• Automatic selection of the most
appropriate books, journals,
and proceedings to market at a
scientific event.

3.
Proceedings classification in a nutshell
3

4.
Smart Semantic Solutions For Springer Nature
4
SBR Engine
CSO
Smart Topic API
Topic
extraction
Smart Topic Miner
Smart Book Recommender
Parser
.zip, .xml
DB
- Topic Taxonomy
- SN codes
- Analytics
Input:
Output:
Publication metadata
GUI
Topic
selection
SN codes
inferenceSN codes
GUI

5.
The Computer Science Ontology consist of about 15,000 topics linked
by about 70,000 semantic relationships. It was automatically created
and is regularly updated using the Klink-2 algorithm.
Osborne, F. and Motta, E.: Klink-2: integrating multiple web sources to generate
semantic topic networks. In ISWC 2015. (2015). Available at http://rdcu.be/wEKy
The Computer Science Ontology

6.
Smart Topic Miner
Smart Topic Miner (STM) is a semantic application designed to
support the Springer Nature Computer Science editorial team
in classifying scholarly publications.
6
6
http://rexplore.kmi.open.ac.uk/STM_demo

7.
http://rexplore.kmi.open.ac.uk/STM_demo
7

8.
Smart Book Recommender
Smart Book Recommender (STM) is a web application that takes
as input a conference and suggests books, proceedings and
journals which address similar topics.
8
http://rexplore.kmi.open.ac.uk/SBR_demo

9.
http://rexplore.kmi.open.ac.uk/SBR_demo
9

10.
Business Value - I
STM is having a multifaceted impact on the SN workflow:
• It halves the time needed for classifying proceedings from 20-
30 to 10-15 minutes.
• It produces 80-90% of correct topics very quickly.
• It allows also assistant editors to work on the classification of
proceedings, distributing the load and reducing costs.
• The adoption of a controlled vocabulary makes the process
more robust and facilitates the identification of related
editorial products.
10

11.
Business Value - II
• It is being applied to the Lecture Notes in Computer Science
(LNCS) and other computer science series (LNBIP, CCIS, IFIP-
AICT, LNICST).
• Since 2016 about 700 proceedings volumes have been
classified with the help of STM, and we are planning to use it
for up to 800 conference proceedings volume per year.
11

12.
Next Steps
• Integrating the STM tool with the SN Linked Open Data portal,
which describes Springer Nature conferences
http://lod.springer.com/
• Performing a comprehensive evaluation of SBR.
• Analysis and enhancement of Springer Nature CS Classification.
• Applying these tools to other research fields, e.g., Life Sciences.
• Opening up the Computer Science Ontology to the wider
scientific community by means of a wiki-like portal.
12

13.
Francesco
Osborne
Angelo
Salatino
Aliaksandr
Birukou
Enrico
Motta
Osborne, F., Salatino, A., Birukou, A. and Motta, E.: Automatic
Classification of Springer Nature Proceedings with Smart Topic
Miner. In ISWC 2016 ). Available at http://rdcu.be/wEHY
Email: francesco.osborne@open.ac.uk
Twitter: FraOsborne
Site: people.kmi.open.ac.uk/francesco
Thiviyan
Thanapalasingam
See also