Identifying Promising Research Topics in Computer Science

AbstractClassifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

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Detecting bursty terms in computer science research

Scientometrics ◽

10.1007/s11192-019-03307-5 ◽

2019 ◽

Vol 122 (1) ◽

pp. 681-699 ◽

Cited By ~ 1

Author(s):

E. Tattershall ◽

G. Nenadic ◽

R. D. Stevens

Keyword(s):

Computer Science ◽

Historical Data ◽

Science Research ◽

Detection Algorithm ◽

Trend Detection ◽

Free Text ◽

Research Topics ◽

Computer Science Research ◽

Insight Into ◽

Over Time

AbstractResearch topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.

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Interaction with geospatial data

it - Information Technology ◽

10.1515/itit-2014-1058 ◽

2015 ◽

Vol 57 (1) ◽

Cited By ~ 1

Author(s):

Johannes Schöning

Keyword(s):

Human Computer Interaction ◽

Computer Science ◽

User Interfaces ◽

Spatial Information ◽

Social Computing ◽

Geospatial Data ◽

Research Interest ◽

Research Topics ◽

Natural User Interfaces ◽

New Methods

AbstractMy research interest lies at the interaction between human-computer interaction (HCI) and geoinformatics. I am interested in developing new methods and novel user interfaces to navigate through spatial information. This article will give a brief overview on my past and current research topics and streams. Generally speaking, geography is playing an increasingly important role in computer science and also in the field of HCI ranging from social computing to natural user interfaces (NUIs). At the same time, research in geography has focused more and more on technology-mediated interaction with spatiotemporal phenomena. By bridging the two fields, my aim is to exploit this fruitful intersection between those two and develop, design and evaluate user interfaces that help people to solve their daily tasks more enjoyable and effectively.

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A Multi-RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency-Based Scientific Influence Modeling

Computational Intelligence and Neuroscience ◽

10.1155/2021/1766743 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Mingying Xu ◽

Junping Du ◽

Zeli Guan ◽

Zhe Xue ◽

Feifei Kou ◽

...

Keyword(s):

Prediction Model ◽

Computer Science ◽

Spatial Attention ◽

Research Field ◽

Research Topic ◽

Research Topics ◽

Semantic Consistency ◽

Model Based ◽

Research Fields ◽

Model Research

Computer science discipline includes many research fields, which mutually influence and promote each other’s development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic representation of a research field. The other is how to model research topic of different fields and keep the semantic consistency of research topics when learning the scientific influence context from other related fields. Unfortunately, the existing research topic prediction approaches cannot handle these two challenges. To solve these problems, we employ multiple different Recurrent Neural Network chains which model research topics of different fields and propose a research topic prediction model based on spatial attention and semantic consistency-based scientific influence modeling. Spatial attention is employed in field topic representation which can selectively extract the attributes from the field topics to distinguish the importance of field topic attributes. Semantic consistency-based scientific influence modeling maps research topics of different fields to a unified semantic space to obtain the scientific influence context of other related fields. Extensive experiment results on five related research fields in the computer science (CS) discipline show that the proposed model is superior to the most advanced methods and achieves good topic prediction performance.

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IN MEMORIAM CHANDRA KINTALA

International Journal of Foundations of Computer Science ◽

10.1142/s0129054112400011 ◽

2012 ◽

Vol 23 (01) ◽

pp. 5-19

Author(s):

MARTIN KAPPES ◽

ANDREAS MALCHER ◽

DETLEF WOTSCHKE

Keyword(s):

Computer Science ◽

Software Reliability ◽

Research Topics ◽

Distant Future ◽

Applied Computer Science ◽

In Memoriam ◽

Limited Nondeterminism

With this contribution we would like to commemorate Chandra M. R. Kintala, who passed away in November 2009. We will give short overviews of his CV and his contributions to the field of theoretical and applied computer science and, given the opportunity, will attempt to present his influence on areas like limited nondeterminism and resources as well as software reliability in an exemplary fashion. Finally, we will briefly touch on some research topics which hopefully will be addressed in the not-so-distant future.

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AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry

Quantitative Science Studies ◽

10.1162/qss_a_00162 ◽

2021 ◽

pp. 1-43

Author(s):

Simone Angioni ◽

Angelo Salatino ◽

Francesco Osborne ◽

Diego Reforgiato Recupero ◽

Enrico Motta

Keyword(s):

Computer Science ◽

Knowledge Graph ◽

Industry Dynamics ◽

Relevant Research ◽

Research Topics ◽

Industrial Sectors ◽

Scholarly Data ◽

Automatic Pipeline ◽

Different Parts

Abstract Academia and industry share a complex, multifaceted, and symbiotic relationship. Analysing the knowledge flow between them, understanding which directions have the biggest potential, and discovering the best strategies to harmonise their efforts is a critical task for several stakeholders. Research publications and patents are an ideal medium to analyze this space, but current datasets of scholarly data cannot be used for such a purpose since they lack a high-quality characterization of the relevant research topics and industrial sectors. In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to the research topics drawn from the Computer Science Ontology. 5.1M publications and 5.6M patents are further characterized according to the type of the author’s affiliations and 66 industrial sectors from the proposed Industrial Sectors Ontology (INDUSO). AIDA was generated by an automatic pipeline that integrates data from Microsoft Academic Graph, Dimensions, DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. It is publicly available under CC BY 4.0 and can be downloaded as a dump or queried via a triplestore. We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results.

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