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Classifying Research Papers with the Computer Science Ontology

Angelo Antonio Salatino, Thiviyan Thanapalasingam, Andrea Mannocci, Francesco Osborne, Enrico Motta
2018 International Semantic Web Conference  
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.  ...  In this paper, we present the CSO Classifier, an application for automatically classifying academic papers according to the rich taxonomy of topics from CSO.  ...  Conclusions In this paper, we presented the CSO Classifier, a tool that automatically classifies text according to the Computer Science Ontology.  ... 
dblp:conf/semweb/SalatinoTMOM18a fatcat:uacfcl4ejvehldfpcyphg6iv2m

Ontology Extraction and Usage in the Scholarly Knowledge Domain [article]

Angelo A. Salatino, Francesco Osborne, Enrico Motta
2020 arXiv   pre-print
In this chapter, we present the Computer Science Ontology (CSO), which is the largest ontology of research areas in the field of Computer Science, and discuss a number of applications that build on CSO  ...  Ontologies of research areas have been proven to be useful in many application for analysing and making sense of scholarly data.  ...  At the fourth layer, we find the CSO Classifier [25] , a tool for automatically classifying research papers according to the topics available in the Computer Science Ontology.  ... 
arXiv:2003.12611v2 fatcat:sxmuv4i5mjaq3db7tlcmy47ue4

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

Angelo A. Salatino, Thiviyan Thanapalasingam, Andrea Mannocci, Aliaksandr Birukou, Francesco Osborne, Enrico Motta
2019 Data Intelligence  
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.  ...  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  ...  The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas In the paper, we also introduced the CSO Classifier, a tool for automatically classifying research papers  ... 
doi:10.1162/dint_a_00055 fatcat:o6xh6hqyrjgchmjkvruu4s4hbi

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

Angelo A. Salatino, Francesco Osborne, Thiviyan Thanapalasingam, Enrico Motta
2019 Lecture Notes in Computer Science  
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  ...  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.  ...  In this paper, we present the CSO Classifier, a new approach for automatically classifying research papers according to the Computer Science Ontology (CSO).  ... 
doi:10.1007/978-3-030-30760-8_26 fatcat:dc4wa4rrrfgvtatlunnb2jirue

The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas [chapter]

Angelo A. Salatino, Thiviyan Thanapalasingam, Andrea Mannocci, Francesco Osborne, Enrico Motta
2018 Lecture Notes in Computer Science  
In this paper, we introduce the Computer Science Ontology (CSO), a largescale, automatically generated ontology of research areas, which includes about 26K topics and 226K semantic relationships.  ...  Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly.  ...  Conclusions In this paper, we presented the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which provides a much more comprehensive and granular characterisation  ... 
doi:10.1007/978-3-030-00668-6_12 fatcat:c3arhzreivd57hi462ibpztktu

Supporting Springer Nature Editors by means of Semantic Technologies

Francesco Osborne, Angelo Antonio Salatino, Aliaksandr Birukou, Thiviyan Thanapalasingam, Enrico Motta
2017 International Semantic Web Conference  
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  ...  products with respect to the relevant research areas and ii) taking informed decisions about their marketing strategy.  ...  Business Value The STM tool has been routinely used by the SN Computer Science editorial team for classifying conference proceedings since January 2017.  ... 
dblp:conf/semweb/OsborneSBTM17 fatcat:crnzhcnocrcbth24tgcyi4vlhe

Automatic Classification of Springer Nature Proceedings with Smart Topic Miner [chapter]

Francesco Osborne, Angelo Salatino, Aliaksandr Birukou, Enrico Motta
2016 Lecture Notes in Computer Science  
STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family.  ...  In this paper 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  ...  Acknowledgements We would like to thank the Springer Nature editors for assisting us in the evaluation of STM.  ... 
doi:10.1007/978-3-319-46547-0_33 fatcat:euaojjigpja7pphn4hqqu5xeti

Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs [article]

Angelo Salatino and Andrea Mannocci and Francesco Osborne
2021 arXiv   pre-print
topics from the Computer Science Ontology.  ...  Analysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research  ...  The CSO Classifier The CSO Classifier [71] is a tool for automatically classifying research papers according to the Computer Science Ontology.  ... 
arXiv:2106.12875v1 fatcat:3gqwimw26zdhrcrghcubcrdr3a

A Proposed Multi-Domain Approach for Automatic Classification of Text Documents

Abdelrahman M. Arab, Ahmed M. Gadallah, Akram Salah
2017 International Journal of Soft Computing  
A document is mapped on each ontology based on the weights of the mutual tokens between them with the help of fuzzy sets, resulting in a mapping degree of the document with each domain.  ...  In this paper an unsupervised classification system is proposed that can manage the Multi-Domain classification problem as well.  ...  American Journal of Medicine 4 3 philosophy Test dataset used in paper [18] 10 10 4 computer science Journal of Computer Sciences 7 10 International Journal of Computer Science & Engineering  ... 
doi:10.5121/ijsc.2017.8101 fatcat:kxqh3idjrbdcfbpbqo643da3gm

A Co-Citation and Cluster Analysis of Scientometrics of Geographic Information Ontology

Yu Liu, Lin Li, Hang Shen, Hui Yang, Feng Luo
2018 ISPRS International Journal of Geo-Information  
Computer science and mathematics play important roles in this field of study.  ...  The papers of Gruber TR and Guarino N are referenced most frequently, as well as that of Smith B., who formally introduced information ontology to the field of geographic information science.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi7030120 fatcat:czv3nh3ko5d3pi4wtpugu7icwu

Finding Hidden Semantics Behind Reference Linkages : An Ontological Approach for Scientific Digital Libraries [chapter]

Peixiang Zhao, Ming Zhang, Dongqing Yang, Shiwei Tang
2005 Lecture Notes in Computer Science  
In this paper, we present a CiteSeer-like system to access scientific papers in computer science discipline by reference linking technique.  ...  on the ontology.  ...  Jian Pei in Department of Computer Science and Engineering, University of Buffalo, the Sate University of New York for his comments on an early version of this paper.  ... 
doi:10.1007/11408079_64 fatcat:ch6rilf7rfarpk4uhvewdxhmsi

Capturing interest through inference and visualization

Stuart E. Middleton, Nigel R. Shadbolt, David C. De Roure
2003 Proceedings of the international conference on Knowledge capture - K-CAP '03  
Our system, called Foxtrot, examines the problem of recommending on-line research papers to academic researchers.  ...  A year long experiment is conducted with over 200 staff and students at the University of Southampton.  ...  Our ontology is based on the CORA [8] digital library, since it classifies computers science topics and has example papers for each class.  ... 
doi:10.1145/945649.945657 fatcat:ljca6rs7kraitdytexmfqcec2m

Capturing interest through inference and visualization

Stuart E. Middleton, Nigel R. Shadbolt, David C. De Roure
2003 Proceedings of the international conference on Knowledge capture - K-CAP '03  
Our system, called Foxtrot, examines the problem of recommending on-line research papers to academic researchers.  ...  A year long experiment is conducted with over 200 staff and students at the University of Southampton.  ...  Our ontology is based on the CORA [8] digital library, since it classifies computers science topics and has example papers for each class.  ... 
doi:10.1145/945645.945657 dblp:conf/kcap/MiddletonSR03 fatcat:hrtm3czoanc5pneigkrud2ez3u

Automating Computer Science Ontology Extension with Classification Techniques

Natasha C. Santosa, Jun Miyazaki, Hyoil Han
2021 IEEE Access  
This study aims to achieve a fully automatic ontology extension. We propose a novel "Direct" approach for extending an existing Computer Science Ontology (CSO).  ...  This approach consists of two steps: initially extending the CSO with new topics and using this extended graph to obtain the new topic's node embeddings as inputs for training classifiers.  ...  Particularly, we experiment with the Computer Science Ontology 2 (CSO) [5] , that stores information about Computer Science research topics.  ... 
doi:10.1109/access.2021.3131627 fatcat:cxgkerhggrafvmd7rc4vj4tzgq

Reducing the effort for systematic reviews in software engineering

Francesco Osborne, Henry Muccini, Patricia Lago, Enrico Motta, James McCusker
2019 Data Science  
Acknowledgements The authors would like to thank the colleagues which donated their time and expertise by contributing to this study as domain experts and/or annotators: Paris Avgeriou, Barbora Buhnova  ...  We also thank Davide Falessi for reviewing an earlier version of this manuscript, and Elsevier BV for providing us with access to its large repository of scholarly data.  ...  All the tested classifiers analysed the title and abstract of the 70 papers and assigned them with a set of topics drawn from the Computer Science Ontology (CSO), a recently released taxonomy of research  ... 
doi:10.3233/ds-190019 fatcat:atsninu2ivatflpd5sphqfngvm
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