Classifying Research Papers with the Computer Science Ontology

Ontologies of research areas are important tools for characterising, exploring and analysing the research landscape. 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. CSO currently powers several tools adopted by the Springer Nature editorial team and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. As an effort to encourage the usage of CSO, we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedbacks at different levels of the ontology. In this paper, we present the CSO Classifier, an application for automatically classifying academic papers according to the rich taxonomy of topics from CSO. The aim is to facilitate the adoption of CSO across the various communities engaged with scholarly data and to foster the development of new applications based on this knowledge base

Detection of Embryonic Research Topics by Analysing Semantic Topic Networks

Being aware of new research topics is an important asset for anybody involved in the research environment, including researchers, academic publishers and institutional funding bodies. In recent years, the amount of scholarly data available on the web has increased steadily, allowing the development of several approaches for detecting emerging research topics and assessing their trends. However, current methods focus on the detection of topics which are already associated with a label or a substantial number of documents. In this paper, we address instead the issue of detecting embryonic topics, which do not possess these characteristics yet. We suggest that it is possible to forecast the emergence of novel research topics even at such early stage and demonstrate that the emergence of a new topic can be anticipated by analysing the dynamics of pre-existing topics. We present an approach to evaluate such dynamics and an experiment on a sample of 3 million research papers, which confirms our hypothesis. In particular, we found that the pace of collaboration in sub-graphs of topics that will give rise to novel topics is significantly higher than the one in the control group

U.S. academic libraries: understanding their web presence and their relationship with economic indicators

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-013-1001-0The main goal of this research is to analyze the web structure and performance of units and services belonging to U.S. academic libraries in order to check their suitability for webometric studies. Our objectives include studying their possible correlation with economic data and assessing their use for complementary evaluation purposes. We conducted a survey of library homepages, institutional repositories, digital collections, and online catalogs (a total of 374 URLs) belonging to the 100 U.S. universities with the highest total expenditures in academic libraries according to data provided by the National Center for Education Statistics. Several data points were taken and analyzed, including web variables (page count, external links, and visits) and economic variables (total expenditures, expenditures on printed and electronic books, and physical visits). The results indicate that the variety of URL syntaxes is wide, diverse and complex, which produces a misrepresentation of academic libraries’ web resources and reduces the accuracy of web analysis. On the other hand, institutional and web data indicators are not highly correlated. Better results are obtained by correlating total library expenditures with URL mentions measured by Google (r = 0.546) and visits measured by Compete (r = 0.573), respectively. Because correlation values obtained are not highly significant, we estimate such correlations will increase if users can avoid linkage problems (due to the complexity of URLs) and gain direct access to log files (for more accurate data about visits).Orduña Malea, E.; Regazzi, JJ. (2014). U.S. academic libraries: understanding their web presence and their relationship with economic indicators. Scientometrics. 98(1):315-336. doi:10.1007/s11192-013-1001-0S315336981Adecannby, J. (2011). 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Search engine results over time—A case study on search engine stability”. Cybermetrics, 2/3. Retrieved February 18, 2013 from http://www.cindoc.csic.es/cybermetrics/articles/v2i1p1.html.Bar-Ilan, J. (2001). Data collection methods on the Web for informetric purposes: A review and analysis. Scientometrics, 50(1), 7–32.Bermejo, F. (2007). The internet audience: Constitution & measurement. New York: Peter Lang Pub Incorporated.Buigues-Garcia, M., & Gimenez-Chornet, V. (2012). Impact of Web 2.0 on national libraries. International Journal of Information Management, 32(1), 3–10.Chu, H., He, S., & Thelwall, M. (2002). Library and information science schools in Canada and USA: A Webometric perspective. Journal of education for Library and Information Science, 43(2), 110–125.Chua, Alton, Y. K., & Goh, D. H. (2010). A study of Web 2.0 applications in library websites. Library and Information Science Research, 32(3), 203–211.Gallego, I., García, I.-M., & Rodríguez, L. (2009). Universities’ websites: Disclosure practices and the revelation of financial information. The International Journal of Digital Accounting Research, 9(15), 153–192.Gomes, B. & Smith, B. T. (2003). Detecting query-specific duplicate documents. [Patent]. Retrieved February 18, 2013 from http://www.patents.com/Detecting-query-specific-duplicate-documents/US6615209/en-US .Harinarayana, N. S., & Raju, N. V. (2010). Web 2.0 features in university library web sites. Electronic Library, 28(1), 69–88.Lewandowski, D., Wahlig, H., & Meyer-Bautor, G. (2006). The freshness of web search engine databases. Journal of Information Science, 32(2), 131–148.Mahmood, K., & Richardson, J. V, Jr. (2012). Adoption of Web 2.0 in US academic libraries: A survey of ARL library websites. Program, 45(4), 365–375.Orduña-Malea, E., & Ontalba-Ruipérez, J-A. (2012). Selective linking from social platforms to university websites: A case study of the Spanish academic system. Scientometrics. (in press).Ortega, J. L., & Aguillo, I. F. (2009). Mapping World-class universities on the Web. Information Processing and Management, 45(2), 272–279.Ortega, José L. & Aguillo, Isidro F. (2009b). North America Academic Web Space: Multicultural Canada vs. The United States Homogeneity. In: ASIST & ISSI pre-conference symposium on informetrics and scientometrics.Phan, T., Hardesty, L., Sheckells, C., & George, A. (2009). Documentation for the academic libraries survey (ALS) public-use data file: Fiscal year 2008. Washington DC: National Center for Education Statistics. Institute of Education Sciences U.S. Department of Education.Qiu, J., Cheng, J., & Wang, Z. (2004). An analysis of backlinks counts and web impact factors for Chinese university websites. Scientometrics, 60(3), 463–473.Regazzi, J. J. (2012a). Constrained?—An analysis of U.S. Academic Libraries and shifts in spending, staffing and utilization, 1998–2008. College and Research Libraries, 73(5), 449–468.Regazzi, J. J. (2012b). Comparing Academic Library Spending with Public Libraries, Public K-12 Schools, Higher Education Public Institutions, and Public Hospitals Between 1998–2008. Journal of Academic Librarianship, 38(4), 205–216.Rousseau, R. (1999). Daily time series of common single word searches in AltaVista and NorthernLight. Cybermetrics, 2/3. Retrieved February 18, 2013 from http://www.cindoc.csic.es/cybermetrics/articles/v2i1p2.html .Sato, S., & Itsumura, H. (2011). How do people use open access papers in non-academic activities? A link analysis of papers deposited in institutional repositories. Library, Information and Media Studies, 9(1), 51–64.Scholze, F. (2007). Measuring research impact in an open access environment. Liber Quarterly: The Journal of European Research Libraries, 17(1–4), 220–232.Smith, A. G. (2011). Wikipedia and institutional repositories: An academic symbiosis? In: Proceedings of the ISSI 2011 conference. Durban, South Africa, 4–7 July 2011. 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AUGUR: Forecasting the Emergence of New Research Topics

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, 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. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall

Plagiarism Detection Avoidance Methods and Countermeasures

Plagiarism is a major problem that educators face in the information age. Today\u27s plagiarist has a near limitless supply of well-written articles via the internet. Due to the scale of the problem, detecting plagiarism has now become the domain of the computer scientist rather than the educator. With the use of computers, documents can be conveniently scanned into a plagiarism detection system that references public web pages, academic journals, and even previous students\u27 papers, acting as an all-seeing eye. However, plagiarists can overcome these digital content detection systems with the use of clever masking and substitutions techniques. These systems cost universities tens of thousands of dollars, and also infringe upon intellectual property ownership rights without the informed consent of individual students. In this work, we examine the efficacy of commercial plagiarism detection systems when used against some selected masking techniques, and then present a simple countermeasure to combat the aforementioned detection avoidance technique

Computers, the internet, and cheating among secondary school students: Some implications for educators

This article investigates in greater depth one particular aspect of cheating within secondary education and some implications for measuring academic achievement. More specifically, it examines how secondary students exploit the Internet for plagiarizing schoolwork, and looks at how a traditional method of educational assessment, namely paper-based report and essay writing, has been impacted by the growth of Internet usage and the proliferation of computer skills among secondary school students. One of the conclusions is that students’ technology fluency is forcing educators to revisit conventional assessment methods. Different options for combating Internet plagiarism are presented, and some software tools as well as non-technology solutions are evaluated in light of the problems brought about by “cyberplagiarism.

How are topics born? Understanding the research dynamics preceding the emergence of new areas

The ability to promptly recognise new research trends is strategic for many stake- holders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the ‘parents’ of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise