The Smart Book Recommender: An Ontology-Driven Application for Recommending Editorial Products

Promoting books and journals to the relevant research communities is an important task for major academic publishers. Unfortunately, identifying which are the best editorial products to market at a certain academic venue is a time-consuming and error-prone process. Here we present the Smart Book Recommender (SBR), an ontology-based recommender that supports the Springer Nature editorial team in selecting the editorial products to market at specific venues. SBR provides an interactive visualisation for analysing the topics characterizing conference series and books. It builds on a dataset of 27K books, journals, and conference proceedings annotated with topics from the Computer Science Ontology, a large-scale ontology of research areas. A user study showed that SBR is able to produce useful recommendations for both editors and researchers

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

Classifying Research Papers with the Computer Science Ontology

Ontologies of research areas are important tools for characterising, exploring and analysing the research landscape. We recently released the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 26K topics and 226K semantic relationships. CSO currently powers several tools adopted by the Springer Nature editorial team and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. As an effort to encourage the usage of CSO, we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedbacks at different levels of the ontology. In this paper, we present the CSO Classifier, an application for automatically classifying academic papers according to the rich taxonomy of topics from CSO. The aim is to facilitate the adoption of CSO across the various communities engaged with scholarly data and to foster the development of new applications based on this knowledge base

Supporting Springer Nature Editors by means of Semantic Technologies

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

Smart Book Recommender: A Semantic Recommendation Engine for Editorial Products

Academic publishers, such as Springer Nature, need to constantly make informed decisions about how and where to market their editorial products. In the field of Computer Science (CS), it is particularly critical to assess which books will be of interest to the attendees of a conference. Typically, these items are manually chosen by publishing editors, on the basis of their personal experience. To make this process both faster and more robust we have developed the Smart Book Recommender (SBR), a semantic application designed to support the Springer Nature editorial team in promoting their publications at CS venues. SBR takes as input the proceedings of a conference and suggests books, journals, and other conference proceedings which are likely to be relevant to the attendees of the conference in question. It does so by taking advantage of a semantic representation of topics, which builds on a very large ontology of Computer Science topics; characterizing Springer Nature books as distributions of semantic topics; and approaching the problem as one of semantic matching between such distributions of semantic topics

The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles

Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods

IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation

Knowledge Graph Embedding (KGE) models are used to learn continuous representations of entities and relations. A key task in the literature is predicting missing links between entities. However, Knowledge Graphs are not just sets of links but also have semantics underlying their structure. Semantics is crucial in several downstream tasks, such as query answering or reasoning. We introduce the subgraph inference task, where a model has to generate likely and semantically valid subgraphs. We propose IntelliGraphs, a set of five new Knowledge Graph datasets. The IntelliGraphs datasets contain subgraphs with semantics expressed in logical rules for evaluating subgraph inference. We also present the dataset generator that produced the synthetic datasets. We designed four novel baseline models, which include three models based on traditional KGEs. We evaluate their expressiveness and show that these models cannot capture the semantics. We believe this benchmark will encourage the development of machine learning models that emphasize semantic understanding

The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualise sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data

Relational graph convolutional networks: a closer look

In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowledge Graph datasets on node classification and link prediction tasks. Our explanation provides a friendly understanding of the different components of the RGCN for both users and researchers extending the RGCN approach. Furthermore, we introduce two new configurations of the RGCN that are more parameter efficient. The code and datasets are available at https://github.com/thiviyanT/torch-rgcn

Smart Book Recommender Evaluation Data

<div>This compressed zip file contains all the data regarding the evaluation study of the Smart Book Recommender (SBR). SBR is a semantically enhanced recommendation engine, developed in collaboration with Springer Nature (SN), for suggesting Springer books, journals and conference proceedings for conferences. SBR characterises conferences in terms of the topics covered in their proceedings and applying cosine pairwise similarity computation to produce a set of recommendations. </div><div><br></div><div>We evaluated SBR on Springer conference series in the field of Computer Science with the help of seven publishing editors and seven researchers from The Open University. The evaluation consisted of two parts: </div><div>1) A quantitative analysis, which focused on analysing how evaluators rated each SBR recommendations for two conference series of their choosing.</div><div>2) A qualitative analysis, which focused on analysing the answers of the surveys that the evaluators completed after a hands-on session with SBR. </div><div><br></div><div>This compressed file contains the following two CSV files: i) Quantitative - Recommendation Ratings, and ii) Qualitative - Survey Responses. Below we describe the datasets.</div