Bibliographic Data Archives - Page 2 of 3

How to use the CSO Classifier in other domains

By Angelo Salatino | 12 September 201916 October 2019• 17 minute read

Being able to characterise research papers according to their topics enables a multitude of high-level applications such as i) categorise proceedings in digital libraries, ii) semantically enhance the metadata of scientific publications, iii) generate recommendations, iv) produce smart analytics, v) detect research trends, and others.

In our recent work, we designed and developed an unsupervised approach to automatically classify research papers according to an ontology of research areas in the field of Computer Science. This approach uses well-known technologies from the field of Natural Language Processing which makes it easily generalisable. In this article, we will show how we can customise the CSO Classifier and apply it to other fields of Science.

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

By Angelo Salatino | 21 August 201912 November 2019• 2 minute read

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.

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

By Angelo Salatino | 06 July 201917 October 2019• 6 minute read

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.

New release: CSO Classifier v2.1

By Angelo Salatino | 19 May 201917 October 2019• 3 minute read

We are pleased to announce that we recently created a new release of the CSO Classifier (v2.1), an application for automatically classifying research papers according to the Computer Science Ontology (CSO). Recently, we have been intensively working on improving its scalability, removing all its bottlenecks and making sure it could be run on large corpus. […]

CSO Classifier

By Angelo Salatino | 01 May 201918 June 2022• 3 minute read

Awesome Scholarly Data Analysis

By Angelo Salatino | 28 February 201915 May 2019• 2 minute read

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, […]

Computer Science Ontology

By Angelo Salatino | 10 January 201918 October 2019• 4 minute read

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.

Invited Talk – Early detection of Research Topics

By Angelo Salatino | 03 August 201804 November 2019• 2 minute read

On 2nd of August 2018, I have been invited by Boris Veytsman, Principal Research Scientist at Chan Zuckerberg Initiative (formerly Meta), to give a talk about my PhD work. Differently from my previous talk to the ORNL group, I had the opportunity to describe my doctoral work more comprehensively. More specifically, I initially showed what is available […]

Invited Talk – AUGUR: Forecasting the Emergence of New Research Topics

By Angelo Salatino | 30 July 201804 November 2019• 2 minute read

On 30th Jul 2018, I have been invited from Dasha Herrmannova, former PhD student at the KMi, to give a talk at the “Machine Learning and Graph Mining for Big Scholarly Data” workshop organised for the Computational Data Analytics Group at Oak Ridge National Laboratory (ORNL). In this talk, named “AUGUR: Forecasting the Emergence of New […]

Supporting Editorial Activities at Springer Nature

By Angelo Salatino | 01 May 201813 November 2019• 6 minute read

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.

Tag: Bibliographic Data