Ontology-Based Recommendation of Editorial Products

Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution.

[1]  Pasquale Lops,et al.  Semantics-Aware Content-Based Recommender Systems , 2014, Recommender Systems Handbook.

[2]  Rafael Valencia-García,et al.  RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes , 2015, Expert Syst. Appl..

[3]  Alexander Maedche,et al.  Ontology-Based User Modeling for Knowledge Management Systems , 2003, User Modeling.

[4]  Enrico Motta,et al.  Klink-2: Integrating Multiple Web Sources to Generate Semantic Topic Networks , 2015, SEMWEB.

[5]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[6]  Evangelos Theodoridis,et al.  Data integration and disintegration: Managing Springer Nature SciGraph with SHACL and OWL , 2017, SEMWEB.

[7]  Enrico Motta,et al.  Exploring Scholarly Data with Rexplore , 2013, International Semantic Web Conference.

[8]  M. Stumptner,et al.  Finding Experts By Semantic Matching of User Profiles , 2008 .

[9]  Silvio Peroni,et al.  Setting our bibliographic references free: towards open citation data , 2015, J. Documentation.

[10]  Enrico Motta,et al.  The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas , 2018, SEMWEB.

[11]  Isabelle Tellier,et al.  Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature , 2016, LREC.

[12]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[13]  Enrico Blanzieri,et al.  A multi-agent system that facilitates scientific publications search , 2006, AAMAS '06.

[14]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[15]  Enrico Motta,et al.  Forecasting the Spreading of Technologies in Research Communities , 2017, K-CAP.

[16]  Zdenek Zdráhal,et al.  CORE: Three Access Levels to Underpin Open Access , 2012, D Lib Mag..

[17]  Bamshad Mobasher,et al.  Web search personalization with ontological user profiles , 2007, CIKM '07.

[18]  Enrico Motta,et al.  Automatic Classification of Springer Nature Proceedings with Smart Topic Miner , 2016, SEMWEB.

[19]  Stuart E. Middleton,et al.  Ontological user profiling in recommender systems , 2004, TOIS.

[20]  Enrico Motta,et al.  Supporting Springer Nature Editors by means of Semantic Technologies , 2017, International Semantic Web Conference.

[21]  Christian Bizer,et al.  DBpedia spotlight: shedding light on the web of documents , 2011, I-Semantics '11.

[22]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[23]  Enrico Motta,et al.  Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance , 2018, SEMWEB.

[24]  Alejandro Bellogín,et al.  A multilayer ontology-based hybrid recommendation model , 2008, AI Commun..

[25]  Kai Eckert,et al.  Springer LOD Conference Portal. Demo paper , 2017, International Semantic Web Conference.

[26]  Horacio Saggion,et al.  Dr. Inventor Framework: Extracting Structured Information from Scientific Publications , 2015, Discovery Science.