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

Salatino, Angelo A.; Osborne, Francesco; Thanapalasingam, Thiviyan; Motta, Enrico

doi:10.1007/978-3-030-30760-8_26

Cited by 57 publications

(85 citation statements)

References 21 publications

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“…For instance, CSO currently includes 684 sub-topics of HCI, while the ACM Classification only contains 36. In addition, the CSO team recently released the CSO classifier, a tool for automatically classifying publications which was shown to generate excellent results [10,15]. Finally, CSO is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) 11 , which facilitates the reproducibility of our work.…”

Section: The Computer Science Ontologymentioning

confidence: 98%

“…We annotated the papers in the IJHCS+CHI dataset by means of the CSO Classifier [15,16], an unsupervised classifier that takes as input the metadata of a research article (title, abstract, and keywords), and returns a selection of the relevant research areas drawn from CSO. A first version of the CSO classifier has been in use since 2016 as a component of the Smart Topic Miner (STM) [17], the tool adopted by Springer Nature to annotate proceedings in the field of Computer Science, and the Smart Book Recommender (SBR) [18], an ontology-based recommender system for editorial products.…”

Section: Automatic Classification Of Research Papers With the Cso Clamentioning

confidence: 99%

“…Mobile Computing grew from 142 publications in CHI during 2009 (14 in IJHCS) to 222 in 2018 (18 in IJHCS, with a peak of 23 in 2017). Its most active subtopics in this period include Smartphones (10 to 61 in CHI), Wearable Computing (15,62), and Mobile Applications (3,19).…”

Section: Research Topic Analysismentioning

confidence: 99%

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The evolution of IJHCS and CHI: A quantitative analysis

Mannocci

Osborne²,

Motta³

2019

International Journal of Human-Computer Studies

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In this paper we focus on the International Journal of Human-Computer Studies (IJHCS) as a domain of analysis, to gain insights about its evolution in the past 50 years and what this evolution tells us about the research landscape associated with the journal. To this purpose we use techniques from the field of Science of Science and analyse the relevant scholarly data to identify a variety of phenomena, including significant geopolitical patterns, the key trends that emerge from a topic-centric analysis, and the insights that can be drawn from an analysis of citation data. Because the area of Human-Computer Interaction (HCI) has always been a central focus for IJHCS, we also include in the analysis the CHI conference, which is the premiere scientific venue in HCI. Analysing both venues provides more data points to our study and allows us to consider two alternative viewpoints on the evolution of HCI research.

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Section: The Computer Science Ontologymentioning

confidence: 98%

Section: Automatic Classification Of Research Papers With the Cso Clamentioning

confidence: 99%

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The evolution of IJHCS and CHI: A quantitative analysis

Mannocci

Osborne²,

Motta³

2019

International Journal of Human-Computer Studies

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“…In [67] the authors make use of feature selection and classification for a semantic information retrieval based on ontologies. The CSO classifier [65] is focused on topic detection in scholarly articles using an ontology-driven approach. Instead, in [2] different information is used based on the vector space model and genetic algorithms to measure the score between the sentences and the weights associated to the features.…”

Section: Related Workmentioning

confidence: 99%

Using a multimedia semantic graph for web document visualization and summarization

Rinaldi

Russo

2020

Multimed Tools Appl

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The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information.

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“…Precision Recall F-Measure C1: STM 2016 (STM Classifier) [7] 80.8% 58.2% 67.6% C2: STM 2018 (CSO Classifier 1.0) [10] 78.3% 63.8% 70.3% C3: STM 2019 (CSO Classifier 2.0) [11] 73.0% 75.3% 74.1%…”

Section: Classifiermentioning

confidence: 99%

Improving Editorial Workflow and Metadata Quality at Springer Nature

Salatino

Osborne

Birukou

et al. 2019

Lecture Notes in Computer Science

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Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world's largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed 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. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements. In this paper we present the most recent version of the tool and describe the evolution of the system over the years, the key lessons learnt, and the impact on the Springer Nature workflow. In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads. We also present a user study involving 9 editors, which yielded excellent results in term of usability, and report an evaluation of the new topic classifier used by STM, which outperforms previous versions in recall and F-measure.

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

Cited by 57 publications

References 21 publications

The evolution of IJHCS and CHI: A quantitative analysis

The evolution of IJHCS and CHI: A quantitative analysis

Using a multimedia semantic graph for web document visualization and summarization

Improving Editorial Workflow and Metadata Quality at Springer Nature

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