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The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas
TLDR
We introduce the Computer Science Ontology (CSO), a large-scale, granular, and automatically generated ontology of research areas, which includes about 26K topics and 226K semantic relationships. Expand
The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas
TLDR
We introduce the Computer Science Ontology (CSO), a large-scale, granular, and automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. Expand
AUGUR: Forecasting the Emergence of New Research Topics
TLDR
We introduce Augur, a novel approach to the early detection of research topics, which is able to detect clusters of topics that exhibit dynamics correlated with emergence of new research topics. Expand
The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles
TLDR
We present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology, a comprehensive ontology of re-search areas in the field of Computer Science. Expand
Automatic Classification of Springer Nature Proceedings with Smart Topic Miner
TLDR
We present Smart Topic Miner (STM), a novel solution which uses semantic web technologies to classify scholarly publications on the basis of a very large automatically generated ontology of research areas. Expand
Classifying Research Papers with the Computer Science Ontology
TLDR
We recently released the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes 26K topics and 226K semantic relationships. Expand
Improving Editorial Workflow and Metadata Quality at Springer Nature
TLDR
We present the most recent version of Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Expand
Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers
TLDR
We compared several classifiers for the supervised distinction between normal elderly and Alzheimer's disease individuals, based on resting state electroencephalographic markers, age, gender and education. Expand
How are topics born? Understanding the research dynamics preceding the emergence of new areas
TLDR
The ability to promptly recognise new research trends is strategic for many stake- holders, including universities, institutional funding bodies, academic publishers and companies. Expand
Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction Based on Innovation-Adoption Priors
TLDR
We propose a novel weakly-supervised approach for the forecasting of new semantic concepts in the research domain, which relies on the incorporation of lexical innovation and adoption priors derived from historical data. Expand
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