Integrating Knowledge Graphs for Comparing the Scientific Output of Academia and Industry

Analysing the relationship between academia and industry allows us to understand how the knowledge produced by the universities is being adopted and enriched by the industrial sector, and ultimately affects society through the release of relevant products and services. In this paper, we present a preliminary approach to assess and compare the research outputs of academia and industry. This solution integrates data from several knowledge graphs describing scientific articles (Microsoft Academics Graph), research topics (Computer Science Ontology), organizations (Global Research Identifier Database), and types of industry (DBpedia). We focus on the Semantic Web as exemplary field and report several insights regarding the different behaviours of academia and industry, and the types of industries most active in this field.

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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 research 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.

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Awesome Scholarly Data Analysis

Awesome Scholarly Data Analysis is a curated collection of resources that can support Scholarly Data analytics. This list ranges from: Datasets, which includes different corpora of papers, citations, authors and others, as well as taxonomies and ontologies of research concepts; Tools for collecting and classifying research papers, information extraction, and visualization; and Venues, Summer Schools, […]

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The Open University and Springer Nature launch the Computer Science Ontology

Springer Nature and the Knowledge Media Institute (KMi) of The Open University are partnering to provide a comprehensive Computer Science Ontology (CSO) to a broad range of communities engaged with scholarly data. CSO can be accessed free of charge through the CSO Portal, a web application that enables users to download, explore, and provide feedback on the ontology.

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Computer Science Ontology

The Computer Science Ontology is a large-scale ontology of research areas that was automatically generated using the Klink-2 algorithm on a dataset of about 16 million publications, mainly in the field of Computer Science. In the rest of the paper, we will refer to this corpus as the Rexplore dataset.
The current version of CSO includes 14,164 topics and 162,121 semantic relationships. The main root is Computer Science; however, the ontology includes also a few secondary roots, such as Linguistics, Geometry, Semantics, and so on.
CSO presents two main advantages over manually crafted categorisations used in Computer Science (e.g., 2012 ACM Classification, Microsoft Academic Search Classification). First, it can characterise higher-level research areas by means of hundreds of sub-topics and related terms, which enables to map very specific terms to higher-level research areas. Secondly, it can be easily updated by running Klink-2 on a set of new publications.

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Are citation networks really acyclic?

Simple answer: no. However, before getting into a more detailed answer, allow me to briefly introduce the concept of citation networks, then I will describe why citation networks cannot be considered acyclic anymore. In the scholarly domain, citation networks is an information network in which each node represents a scientific paper and a link between […]

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Supporting Editorial Activities at Springer Nature

The project aims at fostering Springer Nature editorial activities by supporting them with a variety of smart solutions leveraging artificial intelligence, data mining, and semantic technologies. In particular, the KMi team will support Springer Nature editorial team in classifying proceedings and other editorial products, taking informed decisions about their marketing strategy, and improve their internal classification.

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