Citeseerx: Intelligent Information Extraction and Knowledge Creation from Web-Based Data
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A Comparison of On-Line Computer Science Citation Databases
A Comparison of On-Line Computer Science Citation Databases Vaclav Petricek1,IngemarJ.Cox1,HuiHan2, Isaac G. Councill3, and C. Lee Giles3 1 University College London, WC1E 6BT, Gower Street, London, United Kingdom [email protected], [email protected] 2 Yahoo! Inc., 701 First Avenue, Sunnyvale, CA, 94089 [email protected] 3 The School of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA [email protected] [email protected] Abstract. This paper examines the difference and similarities between the two on-line computer science citation databases DBLP and CiteSeer. The database entries in DBLP are inserted manually while the CiteSeer entries are obtained autonomously via a crawl of the Web and automatic processing of user submissions. CiteSeer’s autonomous citation database can be considered a form of self-selected on-line survey. It is important to understand the limitations of such databases, particularly when citation information is used to assess the performance of authors, institutions and funding bodies. We show that the CiteSeer database contains considerably fewer single author papers. This bias can be modeled by an exponential process with intuitive explanation. The model permits us to predict that the DBLP database covers approximately 24% of the entire literature of Computer Science. CiteSeer is also biased against low-cited papers. Despite their difference, both databases exhibit similar and signif- icantly different citation distributions compared with previous analysis of the Physics community. In both databases, we also observe that the number of authors per paper has been increasing over time. 1 Introduction Several public1 databases of research papers became available due to the advent of the Web [1,22,5,3,2,4,8] These databases collect papers in different scientific disciplines, index them and annotate them with additional metadata. -
Linking Educational Resources on Data Science
The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-19) Linking Educational Resources on Data Science Jose´ Luis Ambite,1 Jonathan Gordon,2∗ Lily Fierro,1 Gully Burns,1 Joel Mathew1 1USC Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, California 90292, USA 2Department of Computer Science, Vassar College, 124 Raymond Ave, Poughkeepsie, New York 12604, USA [email protected], [email protected], flfierro, burns, [email protected] Abstract linked data (Heath and Bizer 2011) with references to well- known entities in DBpedia (Auer et al. 2007), DBLP (Ley The availability of massive datasets in genetics, neuroimag- 2002), and ORCID (orcid.org). Here, we detail the meth- ing, mobile health, and other subfields of biology and ods applied to specific problems in building ERuDIte. First, medicine promises new insights but also poses significant we provide an overview of our project. In later sections, we challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to describe our resource collection effort, the resource schema, Knowledge (BD2K) initiative, funding several centers of ex- the topic ontology, our linkage approach, and the linked data cellence in biomedical data analysis and a Training Coordi- resource provided. nating Center (TCC) tasked with facilitating online and in- The National Institutes of Health (NIH) launched the Big person training of biomedical researchers in data science. A Data to Knowledge (BD2K) initiative to fulfill the promise major initiative of the BD2K TCC is to automatically identify, of biomedical “big data” (Ohno-Machado 2014). The NIH describe, and organize data science training resources avail- BD2K program funded 15 major centers to investigate how able on the Web and provide personalized training paths for data science can benefit diverse fields of biomedical research users. -
Don's Conference Notes
Don’s Conference Notes by Donald T. Hawkins (Freelance Conference Blogger and Editor) <[email protected]> Information Transformation: Open. Global. Plenary Sessions Collaborative: NFAIS’s 60th Anniversary Meeting Regina Joseph, founder of Sibylink (http://www.sibylink.com/) and co-founder of pytho (http://www.pytho.io/), consultancies that Column Editor’s Note: Because of space limitations, this is an specialize in decision science and information design, said that we are abridged version of my report on this conference. You can read the gatekeepers of knowledge and information. Information has never full article which includes descriptions of additional sessions at been more accessible, in demand, but simultaneously under attack. https://against-the-grain.com/2018/04/nfaiss-60th-anniversary- There is both a challenge and an opportunity in information system meeting/. — DTH availability and diversity. News outlets have become organs of influ- ence, and social networks are changing our consumption of information (for example, 26% of news retrieval is through social media). We are In 1958, G. Miles Conrad, director of Biological Abstracts, con- willingly allowing ourselves to be controlled. How will we be able to vened a meeting of representatives from 14 information services to harness the advantages of open access to information when the ability collaborate and cooperate in sharing technology and discussing issues of to access it might be compromised? We need people with multiple mutual interest. The National Federation of Abstracting and Indexing areas of specialist knowledge but who are also connected with broad (now Advanced Information) Services (NFAIS) was formed as a result and general knowledge. -
Analysis of Computer Science Communities Based on DBLP
Analysis of Computer Science Communities Based on DBLP Maria Biryukov1 and Cailing Dong1,2 1 Faculty of Science, Technology and Communications, MINE group, University of Luxembourg, Luxembourg [email protected] 2 School of Computer Science and Technology, Shandong University, Jinan, 250101, China cailing [email protected] Abstract. It is popular nowadays to bring techniques from bibliometrics and scientometrics into the world of digital libraries to explore mecha- nisms which underlie community development. In this paper we use the DBLP data to investigate the author’s scientific career, and analyze some of the computer science communities. We compare them in terms of pro- ductivity and population stability, and use these features to compare the sets of top-ranked conferences with their lower ranked counterparts.1 Keywords: bibliographic databases, author profiling, scientific commu- nities, bibliometrics. 1 Introduction Computer science is a broad and constantly growing field. It comprises various subareas each of which has its own specialization and characteristic features. While different in size and granularity, research areas and conferences can be thought of as scientific communities that bring together specialists sharing simi- lar interests. What is specific about conferences is that in addition to scope, par- ticipating scientists and regularity, they are also characterized by level. In each area there is a certain number of commonly agreed upon top ranked venues, and many others – with the lower rank or unranked. In this work we aim at finding out how the communities represented by different research fields and conferences are evolving and communicating to each other. To answer this question we sur- vey the development of the author career, compare various research areas to each other, and finally, try to identify features that would allow to distinguish between venues of different rank. -
How Do Computer Scientists Use Google Scholar?: a Survey of User Interest in Elements on Serps and Author Profile Pages
BIR 2019 Workshop on Bibliometric-enhanced Information Retrieval How do Computer Scientists Use Google Scholar?: A Survey of User Interest in Elements on SERPs and Author Profile Pages Jaewon Kim, Johanne R. Trippas, Mark Sanderson, Zhifeng Bao, and W. Bruce Croft RMIT University, Melbourne, Australia fjaewon.kim, johanne.trippas, mark.sanderson, zhifeng.bao, [email protected] Abstract. In this paper, we explore user interest in elements on Google Scholar's search engine result pages (SERPs) and author profile pages (APPs) through a survey in order to predict search behavior. We inves- tigate the effects of different query intents (keyword and title) on SERPs and research area familiarities (familiar and less-familiar) on SERPs and APPs. Our findings show that user interest is affected by the re- spondents' research area familiarity, whereas there is no effect due to the different query intents, and that users tend to distribute different levels of interest to elements on SERPs and APPs. We believe that this study provides the basis for understanding search behavior in using Google Scholar. Keywords: Academic search · Google Scholar · Survey study 1 Introduction Academic search engines (ASEs) such as Google Scholar1 and Microsoft Aca- demic2, which are specialized for searching scholarly literature, are now com- monly used to find relevant papers. Among the ASEs, Google Scholar covers about 80-90% of the publications in the world [7] and also provides individual profiles with three types of citation namely, total citation number, h-index, and h10-index. Although the Google Scholar website3 states that the ranking is de- cided by authors, publishers, numbers and times of citation, and even considers the full text of each document, there is a phenomenon called the Google Scholar effect [11]. -
PP-DBLP: Modeling and Generating Public-Private Attributed Networks with DBLP
PP-DBLP: Modeling and Generating Public-Private Attributed Networks with DBLP Xin Huang∗, Jiaxin Jiang∗, Byron Choi∗, Jianliang Xu∗, Zhiwei Zhang∗, Yunya Song# ∗Department of Computer Science, Hong Kong Baptist University #Department of Journalism, Hong Kong Baptist University ∗fxinhuang, jxjian, bchoi, xujl, [email protected], #[email protected] Abstract—In many online social networks (e.g., Facebook, graph is visible only to the corresponding user. From each Google+, Twitter, and Instagram), users prefer to hide her/his users viewpoint, the social network is exactly the union of the partial or all relationships, which makes such private relation- public graph and her/his own private graph. Several sketching ships not visible to public users or even friends. This leads to a new graph model called public-private networks, where each and sampling approaches [2] have been proposed to address user has her/his own perspective of the network including the essential problems of graph processing, such as the size of private connections. Recently, public-private network analysis has reachability tree [5], all-pair shortest paths [6], pairwise node attracted significant research interest in the literature. A great similarities [7], correlation clustering [8] and so on. deal of important graph computing problems (e.g., shortest paths, centrality, PageRank, and reachability tree) has been studied. In social networks, vertices usually contain attributes, e.g., However, due to the limited data sources and privacy concerns, a person has information including name, interests, skills, proposed approaches are not tested on real-world datasets, but and so on. Recent study [2] focuses on one essential aspect on synthetic datasets by randomly selecting vertices as private of topological structure of public-private graphs. -
The Best Nurturers in Computer Science Research
The Best Nurturers in Computer Science Research Bharath Kumar M. Y. N. Srikant IISc-CSA-TR-2004-10 http://archive.csa.iisc.ernet.in/TR/2004/10/ Computer Science and Automation Indian Institute of Science, India October 2004 The Best Nurturers in Computer Science Research Bharath Kumar M.∗ Y. N. Srikant† Abstract The paper presents a heuristic for mining nurturers in temporally organized collaboration networks: people who facilitate the growth and success of the young ones. Specifically, this heuristic is applied to the computer science bibliographic data to find the best nurturers in computer science research. The measure of success is parameterized, and the paper demonstrates experiments and results with publication count and citations as success metrics. Rather than just the nurturer’s success, the heuristic captures the influence he has had in the indepen- dent success of the relatively young in the network. These results can hence be a useful resource to graduate students and post-doctoral can- didates. The heuristic is extended to accurately yield ranked nurturers inside a particular time period. Interestingly, there is a recognizable deviation between the rankings of the most successful researchers and the best nurturers, which although is obvious from a social perspective has not been statistically demonstrated. Keywords: Social Network Analysis, Bibliometrics, Temporal Data Mining. 1 Introduction Consider a student Arjun, who has finished his under-graduate degree in Computer Science, and is seeking a PhD degree followed by a successful career in Computer Science research. How does he choose his research advisor? He has the following options with him: 1. Look up the rankings of various universities [1], and apply to any “rea- sonably good” professor in any of the top universities. -
Tipping Points: Cancelling Journals When Arxiv Access Is Good Enough
Tipping points: cancelling journals when arXiv access is good enough Tony Aponte Sciences Collection Coordinator UCLA Library ASEE ELD Lightning Talk June 17, 2019 Preprint explosion! Brian Resnick and Julia Belluz. (2019). The war to free science. Vox https://www.vox.com/the-highlight/2019/6/3/18271538/open- access-elsevier-california-sci-hub-academic-paywalls Preprint explosion! arXiv. (2019). arXiv submission rate statistics https://arxiv.org/help/stats/2018_by_area/index 2018 Case Study: two physics journals and arXiv ● UCLA: heavy users of arXiv. Not so heavy users of version of record ● Decent UC authorship ● No UC editorial board members 2017 Usage Annual cost Cost per use 2017 Impact Factor Journal A 103 $8,315 ~$80 1.291 Journal B 72 $6,344 ~$88 0.769 Just how many of these articles are OA? OAISSN.py - Enter a Journal ISSN and a year and this python program will tell you how many DOIs from that year have an open access version2 Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. Just how many of these articles are OA? Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. Just how many of these articles are OA? % OA articles from 2017 % OA articles from 2018 Journal A 68% 64% Journal B 11% 8% Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. arXiv e-prints becoming closer to publisher versions of record according to UCLA similarity study of arXiv articles vs versions of record Martin Klein, Peter Broadwell, Sharon E. Farb, Todd Grappone. 2018. Comparing Published Scientific Journal Articles to Their Pre-Print Versions -- Extended Version. -
Microsoft Academic Search and Google Scholar Citations
Microsoft Academic Search and Google Scholar Citations Microsoft Academic Search and Google Scholar Citations: A comparative analysis of author profiles José Luis Ortega1 VICYT-CSIC, Serrano, 113 28006 Madrid, Spain, [email protected] Isidro F. Aguillo Cybermetrics Lab, CCHS-CSIC, Albasanz, 26-28 28037 Madrid, Spain, [email protected] Abstract This paper aims to make a comparative analysis between the personal profiling capabilities of the two most important free citation-based academic search engines, namely Microsoft Academic Search (MAS) and Google Scholar Citations (GSC). Author profiles can be very useful for evaluation purposes once the advantages and the shortcomings of these services are described and taken into consideration. A total of 771 personal profiles appearing in both the MAS and the GSC databases are analysed. Results show that the GSC profiles include more documents and citations than those in MAS, but with a strong bias towards the Information and Computing sciences, while the MAS profiles are disciplinarily better balanced. MAS shows technical problems such as a higher number of duplicated profiles and a lower updating rate than GSC. It is concluded that both services could be used for evaluation proposes only if they are applied along with other citation indexes as a way to supplement that information. Keywords: Microsoft Academic Search; Google Scholar Citations; Web bibliometrics; Academic profiles; Research evaluation; Search engines Introduction In November 2009, Microsoft Research Asia started a new web search service specialized in scientific information. Even though Google (Google Scholar) already introduced an academic search engine in 2004, the proposal of Microsoft Academic Search (MAS) went beyond a mere document retrieval service that counts citations. -
Bibliometric Study of Indian Open Access Social Science Literature Deep Kumar Kirtania [email protected]
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 2018 Bibliometric Study of Indian Open Access Social Science Literature Deep Kumar Kirtania [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/libphilprac Part of the Library and Information Science Commons Kirtania, Deep Kumar, "Bibliometric Study of Indian Open Access Social Science Literature" (2018). Library Philosophy and Practice (e-journal). 1867. http://digitalcommons.unl.edu/libphilprac/1867 Bibliometric Study of Indian Open Access Social Science Literature Deep Kumar Kirtania Library Trainee, West Bengal Secretariat Library & M.Phil Scholar, Department of Library & Information Science University of Calcutta [email protected] Abstract: the purpose of this study is to trace out the growth and development social science literature in open access environment published from India. Total 1195 open access papers published and indexed in Scopus database in ten years have considered for the present study. Research publication from 2008 to 2017 have been analyzed based on literature growth, authorship pattern, activity index, prolific authors and institutions, publication type, channel and citation count have examined to provide a clear picture of Indian social science research. The study shows the dominance of shared authorship and sixty percentages of total articles have been cited. This original research paper described the research productivity of social science in open access context and will be helpful to the social scientist and library professional as a whole. Key Words: Bibliometric study, Research Growth, Social Sciences, Open Access, Scopus, India Introduction: Scholarly communications have been the primary source of creating and sharing knowledge by academics and researchers from the mid 1600s (Chan, Gray & Kahn, 2012). -
Author Template for Journal Articles
Mining citation information from CiteSeer data Dalibor Fiala University of West Bohemia, Univerzitní 8, 30614 Plzeň, Czech Republic Phone: 00420 377 63 24 29, fax: 00420 377 63 24 01, email: [email protected] Abstract: The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including cita- tion and in-degree counts, HITS, PageRank, and its variations based on both the citation and colla- boration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods. Keywords: CiteSeer, citation analysis, rankings, evaluation. Introduction Data from CiteSeer have been surprisingly little explored in the scientometric lite- rature. One of the reasons for this may have been fears that the data gathered in an automated way from the Web are inaccurate – incomplete, erroneous, ambiguous, redundant, or simply wrong. Also, the uncontrolled and decentralized nature of the Web is said to simplify manipulating and biasing Web-based publication and citation metrics. However, there have been a few attempts at processing the Cite- Seer data which we will briefly mention. Zhou et al. -
Scholarly Metrics Recommendations for Research Libraries: Deciphering the Trees in the Forest Table of Contents
EUROPE’S RESEARCH LIBRARY NETWORK Scholarly Metrics Recommendations for Research Libraries: Deciphering the Trees in the Forest Table of Contents 01 3D. Learn About Platforms Before Implementing Them In Services & 15 INTRODUCTION 03 Homogenize Different Sources ABOUT LIBER 3E. Work With Researchers To Build Awareness Of Benefits But Educate 16 1. DISCOVERY & DISCOVERABILITY 05 About Weaknesses of Scholarly Metrics 1A. Provide Contextual Information To Allow the Discovery of Related 05 3F. Expand Your Perspective When Developing Services; Avoid Single- 17 Work & Users Purpose Approaches 1B. Exploit Rich Network Structures & Implement Bibliometric Methods To 07 3G. Teach Colleagues New Skills & Library Goals & Services 17 Enable Discovery 1C. Encourage Sharing Of Library Collections Under Open Licenses 08 4. RESEARCH ASSESSMENT 19 4A. Establish Appropriate Goals for Assessment Exercises Before Selecting 19 2. SHOWCASING ACHIEVEMENTS 09 Databases & Metrics; Be Transparent About Use & Interpretation 2A. Incentivize Researchers To Share Scholarly Works, Promote Achievements 09 4B. Use Different Data Sources to Include Various Scholarly Works & 20 Online & Engage With Audiences Disciplinary Communication & Publication Cultures 2B. Encourage Researchers To Showcase Scientific Contributions & Monitor 11 4C. Rely on Objective, Independent & Commonly-Accepted Data Sources 21 Impact To Provide Sound & Transparent Scholarly Metrics 4D. Avoid Using Composite Indicators & Conflating Different Aspects 21 3. SERVICE DEVELOPMENT 13 Of Scholarly Works & Impact; Don’t Lose The Multifaceted Nature of 3A. Join Forces With Stakeholders To Provide Services Reusing Existing 13 Metrics & Distort Interpretation Resources, Tools, Methods & Data 3B. Value Various Levels of Engagement; Favour Standardized, Well-Established 15 23 CONCLUSION & CALL FOR ACTION Practices & Easy-To-Use Tools 25 REFERENCES 3C.