=Paper=
{{Paper
|id=Vol-1963/paper490
|storemode=property
|title=Supporting Springer Nature Editors by means of Semantic Technologies
|pdfUrl=https://ceur-ws.org/Vol-1963/paper490.pdf
|volume=Vol-1963
|authors=Francesco Osborne,Angelo Antonio Salatino,Aliaksandr Birukou,Thiviyan Thanapalasingam,Enrico Motta
|dblpUrl=https://dblp.org/rec/conf/semweb/OsborneSBTM17
}}
==Supporting Springer Nature Editors by means of Semantic Technologies==
https://ceur-ws.org/Vol-1963/paper490.pdf
Supporting Springer Nature Editors
by means of Semantic Technologies
Francesco Osborne1, Angelo Salatino1, Aliaksandr Birukou2,
Thiviyan Thanapalasingam1, Enrico Motta1
1
Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK
{francesco.osborne,angelo.salatino,thiviyan.thanapalasingam,enrico.motta}
@open.ac.uk
2
Springer-Verlag GmbH, Tiergartenstrasse 17, 69121 Heidelberg, Germany
aliaksandr.birukou@springer.com
Abstract. The Open University and Springer Nature have been collaborating since
2015 in the development of an array of semantically-enhanced solutions supporting
editors in i) classifying proceedings and other editorial products with respect to the
relevant research areas and ii) taking informed decisions about their marketing
strategy. These solutions include i) the Smart Topic API, which automatically maps
keywords associated with published papers to semantically characterized topics,
which are drawn from a very large and automatically-generated ontology of
Computer Science topics; ii) the Smart Topic Miner, which helps editors to associate
scholarly metadata to books; and iii) the Smart Book Recommender, which assists
editors in deciding which editorial products should be marketed in a specific venue.
Keywords: Scholarly Data, Ontology Learning, Bibliographic Data, Scholarly
Ontologies, Data Mining, Conference Proceedings, Metadata, Classification.
1 Classifying Proceedings using Semantic Technologies
Correctly classifying proceedings and other editorial products in terms of the relevant
research areas is critical to facilitate their discovery and to allow editors to take informed
decisions about where to market them. Traditionally, this process has been handled
manually by experienced editors, leading to high costs and slow throughput. In this short
paper, we present a number of solutions informed by Semantic Technologies, which we
have developed to address this issue in the context of a collaboration between Springer
Nature (SN) and the Knowledge Media Institute (KMi) of The Open University. These
solutions include: i) the Smart Topic API, ii) the Smart Topic Miner, and iii) the Smart
Book Recommender.
Purely syntactic solutions, which extract frequent keyphrases from a set of documents,
have shown to be limited for classifying conference proceedings [1], since they typically
return a very large and unwieldy distribution of terms. The Smart Topic API addresses
this issue by mapping these terms to concepts in the Computer Science Ontology (CSO)
and returning a human-friendly number of structured topic descriptors. CSO is a large
scale and granular ontology of research topics that has been created automatically, by
running the Klink-2 algorithm [2] on the Rexplore dataset [3]. This consists of about 16
million publications in the field of Computer Science. CSO includes about 17K topics,
which are linked by 70K semantic relationships. The mappings from keywords to
concepts take into account both synonyms and sub-areas of a topic – e.g., all documents
associated to terms such as “semantic technologies”, “linked data”, “RDF”, “OWL” will
be also tagged with the topic “Semantic Web”. The result is a balanced topic distribution,
which is both accurate in terms of topic annotation and also easy to understand and edit
for the user.
The Smart Topic API, which is also available for research purposes, currently supports
two web applications. The first is the Smart Topic Miner (STM) 1 [1], which helps editors
in understanding and classifying proceedings i) by suggesting, for each proceeding, both a
structured set of relevant research topics drawn from CSO, as well as a set of codes drawn
from the SN Classification for Computer Science, and ii) by providing editors with an
environment to make sense of the proposed annotations and edit them if necessary. The
second application is the Smart Book Recommender 2 [4], which takes as input the
proceedings of a conference and returns books, journals and other proceedings that are
likely be of interest for its attendees. It does so by computing the similarity of SN editorial
products over the vectors of semantic topics returned by the Smart Topic API.
2 Business Value
The STM tool has been routinely used by the SN Computer Science editorial team for
classifying conference proceedings since January 2017. It is being applied to the Lecture
Notes in Computer Science (LNCS) and other computer science series (LNBIP, CCIS,
IFIP-AICT, LNICST), which publish about 780 volumes each year.
STM halves the time needed for classifying proceedings from 20-30 to 10-15 minutes.
The benefits are especially evident when classifying complex multi-volume conferences,
such as the European Conference on Computer Vision (ECCV3). The perceived value of
STM can be roughly described as “it helps one to get 80-90% of topics correct very
quickly”. Indeed, while the classification of proceedings has traditionally been performed
only by very experienced editors, thanks to STM it is now possible for assistant editors to
perform the task, thus distributing the load and reducing costs. In addition, the adoption of
a controlled vocabulary (in terms of the CSO ontology) makes the process more robust
and facilitates the identification of related editorial products.
In the future, we plan to integrate the STM tool with the SN Linked Open Data portal4,
which describes Springer Nature conferences [5]. This will allow users to formulate
complex queries that take advantage of the granular topic taxonomy provided by CSO.
In conclusion, we believe that this project offers an excellent example of how the use
of ontologies and other semantic technologies can be effectively deployed in an
organization to make workflows more robust and reduce costs.
References
1. Osborne, F., Salatino, A., Birukou, A., Motta, E.: Automatic Classification of Springer Nature
Proceedings with Smart Topic Miner. In International Semantic Web Conference 2016 (pp.
383-399). Springer. (2016)
2. Osborne, F., Motta, E.: Klink-2: integrating multiple web sources to generate semantic topic
networks. In International Semantic Web Conference 2015 (pp. 408-424). Springer. (2015)
3. Osborne, F., Motta, E. and Mulholland, P.: Exploring scholarly data with Rexplore. In
International Semantic Web Conference 2013 (pp. 460-477). Springer. (2013)
4. Osborne, F., Thanapalasingam, T., Salatino, A., Birukou, A., Motta, E.: Smart Book
Recommender: A Semantic Recommendation Engine for Editorial Products. In International
Semantic Web Conference 2017 (Poster Track).
5. Bryl, V., Birukou, A., Eckert, K., Kessler, M.: What’s in the proceedings? Combining
publisher’s and researcher’s perspectives. In SePublica 2014 (2014)
1
Demo available at http://rexplore.kmi.open.ac.uk/STM2_demo/
2
Demo available at http://rexplore.kmi.open.ac.uk/SBR-demo
3
https://link.springer.com/conference/eccv
4
http://lod.springer.com/