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 page, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according […]
The AIDA Dashboard is a tool for exploring and making sense of scientific conferences which integrates statistical analysis, semantic technologies, and visual analytics. The dashboard was developed in collaboration with Springer Nature for assisting editors in assessing conferences, but it also supports several other use cases. Compared to other state-of-the-art solutions, it introduces three novel […]
The SKM3 team at the OU’s Knowledge Media Institute and Springer Nature have signed a new agreement, extending their collaboration in the area of scholarly analytics until January 2021. The collaboration between the SKM3 team and Springer Nature started in 2014 and has continued without interruptions for the past 5 years. This partnership focuses on […]
Smart Topic Miner Smart Topic Miner (STM) is an application that assists the Springer Nature editorial team in classifying the scientific literature in Computer Science in terms of a catalogue of about 15K research topics, with a very high degree of accuracy. This catalogue was itself generated by our team using a highly innovative knowledge […]
The Computer Science Ontology (CSO) is a large-scale ontology of research areas that was automatically generated using the Klink-2 algorithm [1] on the Rexplore dataset [2], which consists of about 16 million publications, mainly in the field of Computer Science. The Klink-2 algorithm combines semantic technologies, machine learning, and knowledge from external sources to automatically […]
The Technology-Topic Framework (TTF) is a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. TTF characterizes technologies in terms of a set of topics drawn from a large-scale ontology of research areas over a given time period and applies machine learning on these data to forecast […]
Augur is a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Augur operates in three steps. First, it creates evolutionary networks describing the collaboration between research topics over […]
Rexplore leverages novel solutions in large-scale data mining, semantic technologies and visual analytics, to provide an innovative environment for exploring and making sense of scholarly data. In particular, Rexplore allows users: To detect and make sense of important trends in research, such as, significant migrations of researchers from one area to another, the emergence of […]
The Smart Book Recommender(SBR) is semantic application designed to support the Springer Nature editorial team in promoting their publications at Computer Science venues. It 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 […]
Klink-2 is an application which takes as input large amounts of scholarly metadata and automatically generates an OWL ontology containing all the research areas mined from the input data and their semantic relationships. It was developed to produced large scale ontology of research topics. The traditional way to address the problem of identifying and […]
Last week, Francesco, Angelo, and Tanay attended the 23rd International Semantic Web Conference (ISWC) in Baltimore, Maryland. ISWC is the premier conference in the field of Semantic Web, known for its influence and presentation of the latest trends in the field. The main theme of the conference focused on integrating Knowledge Graphs and Large Language Models (LLMs), such as GraphRAG and...
On 1 October 2024, KMi’s Scholarly Knowledge Modelling (SKM) team welcomed Michael McCoubrey, who is starting his part-time doctoral research under the supervision of Dr Angelo Salatino, Dr Francesco Osborne, and Prof Enrico Motta. Michael will investigate novel solutions for automatic hypotheses generation using AI. As a Data Scientist at The Open University, Michael brings expertise in...
Abstract Since their release, both the Computer Science Ontology and the CSO Classifier have received growing attention. They are being employed within several applications and proved to effectively support a wide range of tasks, including trend analysis, enhancing metadata, and assessing impact. Read this article in PDF 1. Introduction The Computer Science Ontology (CSO)...
KMi is delighted to showcase its influential participation at SEMANTiCS 2024. Dr. Angelo Salatino and Dr. Francesco Osborne were actively involved in organizing the conference, a process that spanned the past eight months. This year, SEMANTiCS celebrated its twentieth anniversary, taking place from 17th to 19th September in Amsterdam, at the stunning Meervaart theatre. The SEMANTiCS...
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