Citation Linkages Research Articles

Overton: A bibliometric database of policy document citations

Abstract This paper presents an analysis of the Overton policy document database, describing the makeup of materials indexed and the nature in which they cite academic literature. We report on various aspects of the data, including growth, geographic spread, language representation, the range of policy source types included, and the availability of citation links in documents. Longitudinal analysis over established journal category schemes is used to reveal the scale and disciplinary focus of citations and determine the feasibility of developing field-normalized citation indicators. To corroborate the data indexed, we also examine how well self-reported funding outcomes collected by UK funders correspond to data indexed in the Overton database. Finally, to test the data in an experimental setting, we assess whether peer-review assessment of impact as measured by the UK Research Excellence Framework (REF) 2014 correlates with derived policy citation metrics. Our findings show that for some research topics, such as health, economics, social care, and the environment, Overton contains a core set of policy documents with sufficient citation linkage to academic literature to support various citation analyses that may be informative in research evaluation, impact assessment, and policy review.

Robotics in Industry 4.0: A Bibliometric Analysis (2011-2022)

Robotics forms an integral part of industry 4.0, the industrial revolution of the 21st century. This paper presents a bibliometric analysis of Web of Science (WoS) indexed publications addressing this emerging field from 2011 till June 2022. WoS research publications were firstly analysed along multiple verticals such as annual counts, types, publishing sources, research directions, researchers, organizations, and countries. Next, co-authorship collaborations among authors, organizations, and countries were discovered. This was followed by an analysis of co-occurring keywords related to robotics in industry 4.0. Finally, a detailed citation analysis was carried out to unearth citation linkages among authors, institutions, documents, nations, and journals. Latest trends, under-investigated topics, and future directions are also discussed. Primary results indicate that more than 3000 articles are being published annually in this emerging field, with a total of 18,893 documents published in WoS during the last decade. The 'IEEE Access', Chinese Academy of Science, Wang Y. (USA), and the USA emerged as the topmost productive journal, institution, author, and nation. Porpiglia Francesco (Italy), Chinese Academy Science and USA obtained the highest co-authorship total link strength (TLS); whereas Lee Chengkuo (Singapore), China, Chinese Academy Science, and the IEEE Access scored the highest citation TLS among authors, countries, organizations, and sources respectively. Machine learning (ML) emerged as the highest co-occurring keyword, followed by artificial intelligence (AI). Computer Science emerged as the most trending research domain, followed by general applications. In the future, ML and AI will advance more sophisticated robots in industry 4.0 systems.

A Bibliometric Analysis of Fintech Trends and Digital Finance

Digital finance has piqued the curiosity of academics, students, and institutions all around the globe for more than a decade. Innovative financial services companies are offering a wide range of new financial products and new ways of interacting with customers via digital finance (Fintech). Research on finance and information systems has thus examined these shifts as well as the implications of technological advancements on the financial industry. Through presenting a bibliometric analysis, the article summarizes how scientific research has developed on the connections between financial technology developments and digital finance during the previous years. According to the ScienceDirect database, we base this literature review on journals and articles that have been published. We conducted a content analysis of 343 articles based on the discovered clusters, finding research gaps and suggesting actionable areas for further study. The results offer a solid path for future research in this area. We discuss the significance of the aforementioned publications and articles as well as potential areas of future study. The next step is to analyze the citation linkages between the most important articles to identify how they are related to one another. For financial technology research, the study looks at the way they are organized. The research is concerned with the roles of Fintech and the limits of research in digital financing. We point out potential routes for researchers to take to expand on current knowledge while also seeking possibilities for new, interesting, and creative research that adds to the expansion of the topic of research.

Semi-automated Tools for Systematic Searches.

Traditionally, literature identification for systematic reviews has relied on a two-step process: first, searching databases to identify potentially relevant citations, and then manually screening those citations. A number of tools have been developed to streamline and semi-automate this process, including tools to generate terms; to visualize and evaluate search queries; to trace citation linkages; to deduplicate, limit, or translate searches across databases;and to prioritize relevant abstracts for screening. Research is ongoing into tools that can unify searching and screening into a single step, and several protype tools have been developed. As this field grows, it is becoming increasingly important to develop and codify methods for evaluating the extent to which these tools fulfill their purpose.

Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain

Abstract Main path analysis has been widely used in various fields to detect their development trajectories. However, the previous methods treat every citation equally. In fact, it leaves a question open to scholars considering that there are different citation preferences in different disciplines and at different publication times. There are different citation preferences in different disciplines and at different periods, which are ignored by scholars. In order to deal with the problem in identifying development paths in interdisciplinary research areas, this paper proposes a new main path analysis method. The improved main path analysis considers two factors involved in citation preference, including discipline bias and time bias. An evidence analysis from blockchain domain is conducted to demonstrate the effectiveness of the proposed method. The research result shows that the proposed main path analysis method in this paper can resolve the problem of discipline bias and time bias in interdisciplinary research. Moreover, the improved method provides a more differentiated ranking for citation linkages in the network. Our research can enhance the objectivity of the resulting main paths and promote broader application of the main path analysis.

An analysis of the evolution of science-technology linkage in biomedicine

Demonstrating the practical value of public research has been an important subject in science policy. Here we present a detailed study on the evolution of the citation linkage between life science related patents and biomedical research over a 37-year period. Our analysis relies on a newly-created dataset that systematically links millions of non-patent references to biomedical papers. We find a large disparity in the volume of science linkage among technology sectors, with biotechnology and drug patents dominating it. The linkage has been growing exponentially over a long period of time, doubling every 2.9 years. The U.S. has been the largest producer of cited science for years, receiving nearly half of the citations. More than half of citations goes to universities. We use a new paper-level indicator to quantify to what extent a paper is basic research or clinical medicine. We find that the cited papers are likely to be basic research, yet a significant portion of papers cited in patents that are related to FDA-approved drugs are clinical research. The U.S. National Institute of Health continues to be an important funder of cited science. For the majority of companies, more than half of citations in their patents are authored by public research. Taken together, these results indicate a continuous linkage of public science to private sector inventions.

The domestic localization of knowledge flows as evidenced by publication citation: the case of Italy

This work applies a new approach to measure knowledge flows. Assuming that citation linkages between articles imply a flow of knowledge from the cited to the citing authors, we investigate the geographic flows of scientific knowledge produced in Italy across its regions, at both overall and field level. Furthermore, we measure the the specialization indexes for outflows and inflows of knowledge by a given region. Findings show that larger regions in terms of research output are more likely net exporters of new knowledge. At the same time, we register a positive correlation between the share of intraregional flows and the size of overall scientific output of a region.

Paper-patent citation linkages as early signs for predicting delayed recognized knowledge: Macro and micro evidence

Abstract In this study, we investigate the extent to which patent citations to papers can serve as early signs for predicting delayed recognized knowledge in science using a comparative study with a control group, i.e., instant recognition papers. We identify the two opposite groups of papers by the Bcp measure, a parameter-free index for identifying papers which were recognized with delay. We provide a macro (Science/Nature papers dataset) and micro (a case chosen from the dataset) evidence on paper-patent citation linkages as early signs for predicting delayed recognized knowledge in science. It appears that papers with delayed recognition show a stronger and longer technical impact than instant recognition papers. We provide indication that in the more recent years papers with delayed recognition are awakened more often and earlier by a patent rather than by a scientific paper (also called “prince”). We also found that patent citations seem to play an important role to avoid instant recognition papers to level off or to become a so called “flash in the pan”, i.e., instant recognition. It also appears that the sleeping beauties may firstly encounter negative citations and then patent citations and finally get widely recognized. In contrast to the two focused fields (biology and chemistry) for instant recognition papers, delayed recognition papers are rather evenly distributed in biology, chemistry, psychology, geology, materials science, and physics. We discovered several pairs of “science sleeping”-“technology inducing”, such as “biology-biotechnology/pharmaceuticals”, “chemistry-chemical engineering”, as well as some trans-fields science-technology interactions, such as “psychology - computer technology/control technology/audio-visual technology”, “physics - computer technology”, and “mathematics-computer technology”. We propose in further research to discover the potential ahead of time and transformative research by using citation delay analysis, patent & NPL analysis, and citation context analysis.

Research on the Relevancy of Scientific Literature Based on the Citation-Mention Frequency

All papers cite references, but not all citations are equal. This is because references have different citation-mention frequencies. Some references are mentioned only once, while some are mentioned several times. Papers are cited by others when they are relevant to the citing paper. The fact that a reference has a high citation-mention frequency may mean that its content is more closely related to the citing paper. From this point of view, we examined the relevancy of a cited paper to a citing paper on the basis of citation-mention frequency. Two aspects of relevancy are considered: citation linkage and content. We construct a highly mentioned class of references and a rarely mentioned class of references. We introduce the concepts of “reference-similarity” and “content-similarity.” First, we count the number of co-cited references and calculate the reference-similarity. Second, we extract the abstracts of papers and calculate the content-similarity using the bag-of-words model. The results show that references from the highly mentioned class are more relevant to the citing papers than those from the rarely mentioned class.

The choice of examiner patent citations for refusals: evidence from the trilateral offices

Utilizing a novel methodology based on international family-to-family patent citation data, this paper directly compares X/Y patent citations (i.e., those cited as grounds for rejections, equivalent to “blocking patents” in the US) between major patent offices. Remarkable discrepancies between the offices were revealed, despite the common patentability criteria of novelty and inventive step to generate citations. This paper then introduces a simple cosine similarity measurement between a family of X/Y patent citations and all citations added globally to the same original application. How the discrepancies of X/Y patent citations at the European Patent Office (EPO) and the US Patent and Trademark Office (USPTO) relate to the characteristics of applications and longitudinal aspects of office actions were also examined. X/Y patent citations from both the EPO and USPTO commonly show that the range of patent application classes is positively correlated with divergent reasons for refusal, suggesting that costly examinations lead to diversified X/Y patent citations. One novel methodological feature of this paper is that examiner citations across jurisdictions are comparable if we employ family-to-family citations and common criteria for the X/Y citation category. Furthermore, unlike the normal citation-generating process where a citing document adds citations to prior art only once, this paper represents the first attempt to analyze a citation network with multiple citing opportunities from separate parties. We find that the variance of citation linkages has a negative relationship with the ease in which different citers evaluate prior art in the same way, thereby providing a new perspective on the notion of breadth in citation impact.

Computational linguistics literature and citations oriented citation linkage, classification and summarization

Scientific literature is currently the most important resource for scholars, and their citations have provided researchers with a powerful latent way to analyze scientific trends, influences and relationships of works and authors. This paper is focused on automatic citation analysis and summarization for the scientific literature of computational linguistics, which are also the shared tasks in the 2016 workshop of the 2nd Computational Linguistics Scientific Document Summarization at BIRNDL 2016 (The Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries). Each citation linkage between a citation and the spans of text in the reference paper is recognized according to their content similarities via various computational methods. Then the cited text span is classified to five pre-defined facets, i.e., Hypothesis, Implication, Aim, Results and Method, based on various features of lexicons and rules via Support Vector Machine and Voting Method. Finally, a summary of the reference paper from the cited text spans is generated within 250 words. hLDA (hierarchical Latent Dirichlet Allocation) topic model is adopted for content modeling, which provides knowledge about sentence clustering (subtopic) and word distributions (abstractiveness) for summarization. We combine hLDA knowledge with several other classical features using different weights and proportions to evaluate the sentences in the reference paper. Our systems have been ranked top one and top two according to the evaluation results published by BIRNDL 2016, which has verified the effectiveness of our methods.

Citations in Life Science Patents to Publicly Funded Research at Academic Medical Centers.

The contributions of Academic Medical Centers (AMCs) to biomedical innovation have been difficult to measure because of the challenges involved in tracing knowledge flows from their origin to their uses. The authors examined patent citation linkages between AMC research funded by the National Institutes of Health (NIH) and patents. In prospective analyses, they examine the extent to which articles resulting from NIH grants to AMCs awarded between 1990 and 1995 were cited in drug and medical patents. The authors then examine the extent to which these patents are associated with marketed drugs. In retrospective analyses, they examine the share of drugs approved between 2000 and 2009 that have citation links to NIH-funded AMC research. The prospective analyses show over a third of AMC grants resulted in publications that were cited in patents. Most the patents are drug and biotechnology patents, and are assigned to private firms. Patents citing NIH-funded AMC publications were associated with 106 new FDA approved drugs, half of which are new molecular entities and a quarter of which are priority NMEs.The retrospective analyses showed that about half of the new molecular entities approved over the 2000-2009 period had citations links to NIH-funded AMC research. There are strong links between articles from NIH-funded AMC research and private sector medical patenting, including drugs. More research is needed to better understand the types of links the citations represent and their implications for public policy.

Topic based classification and pattern identification in patents

Patent classification systems and citation networks are used extensively in innovation studies. However, non-unique mapping of classification codes onto specific products/markets and the difficulties in accurately capturing knowledge flows based just on citation linkages present limitations to these conventional patent analysis approaches. We present a natural language processing based hierarchical technique that enables the automatic identification and classification of patent datasets into technology areas and sub-areas. The key novelty of our technique is to use topic modeling to map patents to probability distributions over real world categories/topics. Accuracy and usefulness of our technique are tested on a dataset of 10,201 patents in solar photovoltaics filed in the United States Patent and Trademark Office (USPTO) between 2002 and 2013. We show that linguistic features from topic models can be used to effectively identify the main technology area that a patent's invention applies to. Our computational experiments support the view that the topic distribution of a patent offers a reduced-form representation of the knowledge content in a patent. Accordingly, we suggest that this hidden thematic structure in patents can be useful in studies of the policy–innovation–geography nexus. To that end, we also demonstrate an application of our technique for identifying patterns in technological convergence.

Combined biological and health effects of electromagnetic fields and other agents in the published literature

Electromagnetic field (EMF) radiation exerts both stand-alone and combined effects on biological systems. The present study examines the scope of the combined effects; i.e., identify effects on biological systems from combined exposure to electromagnetic fields/radiation and at least one other agent. Only articles in which the presence of EMF had some effect (beneficial or adverse) on the biological system were selected. A comprehensive and novel query was developed using an iterative hybrid approach, whereby articles related by common text and by citation linkages were retrieved. This retrieved literature was: 1) clustered algorithmically into 32 biomedical sub-themes (assigned by the authors); 2) grouped through factor analysis into 32 factors; and 3) subsequently grouped manually (by the authors) into an effects-based taxonomy. The common principles within each thematic cluster/group that accounted for the combined effects were identified.There is a wide range of potential effects in which EMF plays a supportive role. Beneficial effects include improved treatment of chronic diseases like cancer by enhancing ionizing radiation or chemotherapy, and accelerated healing of wounds and injuries in concert with other agents. Adverse effects, on the other hand, include enhanced carcinogenesis, cellular or genetic mutations, and teratogenicity. It should be noted that community consensus does not exist on these potential effects, either beneficial or adverse, although there is substantial credible scientific evidence supporting the above effects (as the body of this paper shows). In real life, the body is exposed to multiple environmental agents simultaneously, e.g., a variety of EMF, pesticides, food additives, and air pollution. The number of potential environmental agent combinations is large, and each combination could potentially have beneficial or adverse effects; much work remains to be done before definitive statements about EMF safety can be made.

Evaluations of context-based co-citation searching

Since machine-readable documents have become widespread, some recent studies have proposed retrieval methods using a combination of citation linkage and its context. In the case of co-citation linkage, there have been attempts to discern `strong' co-citations from `weak' ones by examining the positions of citations in a document. However, this promising concept has not yet been sufficiently evaluated, and it remains unclear whether search performance is significantly improved. Therefore, this paper explores the effects of using co-citation context more deeply and more widely by comparing the search performance of six retrieval methods, which differ as to whether co-citation context and normalization using cited frequency are used. For empirically evaluating the effects, a special test collection was created from CiteSeer Metadata, and the search performances of the six retrieval methods were compared by two IR metrics (AP and nDCG). The main conclusions of this paper are: (1) co-citation context has a positive effect on co-citation searching; (2) the normalization technique using cited frequency is useful for context-based co-citation searching; (3) approaches of using co-citation context tend to affect the characteristics of search performance.

Mapping the Multidisciplinary Field of Public Health Services and Systems Research

Public health services and systems research (PHSSR) is the field of study charged with evaluating the public health system. PHSSR currently lacks a clear identity integrating the many theories, approaches, and disciplines contributing to the field. Experts in PHSSR were consulted to identify 11 key published PHSSR studies. With these articles as a starting point, a newly developed citation data collection system was used to collect a sample of 2986 documents connected to the key articles through citation linkages. Data were collected in October 2009. Citation network methods and latent position cluster modeling were used to examine the network of documents. A subset of 108 documents comprising the backbone of the network was identified through main-path analysis. Four unique clusters were identified within the main path. The core cluster consisted of older articles focused on local health department activities, partnerships, and effectiveness. The three non-core clusters focused on public health law, behavioral interventions, and national performance standards. Although all non-core clusters cited the core, there was little crosstalk among the non-core clusters, a pattern consistent with multidisciplinary fields. PHSSR appears to be a multidisciplinary field with research happening in silos across different research areas. Developing transdisciplinary research connections across PHSSR is necessary to meet national PHSSR goals.

Development of computer science disciplines: a social network analysis approach

In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.

A Comparative Study of Linkage Indexes: Co-assignee, Reciprocal Citation, Patent Coupling and Co-patent

Four indexes including co-assignees, reciprocal citation, patent coupling and co-patent were examined in this study to reveal the meanings of the correlations generated via different citation linkages. This study includes 6,274 genetic engineering patents, and 16 primary assignees identified by Bradford model analysis as the base for correlation analysis. The results show that there are four cluster types, including technological affiliated, technological competitor correlated, commercial collaborated and technological isolated.

Mining and modeling linkage information from citation context for improving biomedical literature retrieval

Mining linkage information from the citation graph has been shown to be effective in identifying important literatures. However, the question of how to utilize linkage information from the citation graph to facilitate literature retrieval still remains largely unanswered. In this paper, given the context of biomedical literature retrieval, we first conduct a case study in order to find out whether applying PageRank and HITS algorithms directly to the citation graph is the best way of utilizing citation linkage information for improving biomedical literature retrieval. Second, we propose a probabilistic combination framework for integrating citation information into the content-based information retrieval weighting model. Based on the observations of the case study, we present two strategies for modeling the linkage information contained in the citation graph. The proposed framework provides a theoretical support for the combination of content and linkage information. Under this framework, exhaustive parameter tuning can be avoided. Extensive experiments on three TREC Genomics collections demonstrate the advantages and effectiveness of our proposed methods.

Mapping the structure and evolution of chemistry research

How does our collective scholarly knowledge grow over time? What major areas of science exist and how are they interlinked? Which areas are major knowledge producers; which ones are consumers? Computational scientometrics – the application of bibliometric/scientometric methods to large-scale scholarly datasets – and the communication of results via maps of science might help us answer these questions. This paper represents the results of a prototype study that aims to map the structure and evolution of chemistry research over a 30 year time frame. Information from the combined Science (SCIE) and Social Science (SSCI) Citations Indexes from 2002 was used to generate a disciplinary map of 7,227 journals and 671 journal clusters. Clusters relevant to study the structure and evolution of chemistry were identified using JCR categories and were further clustered into 14 disciplines. The changing scientific composition of these 14 disciplines and their knowledge exchange via citation linkages was computed. Major changes on the dominance, influence, and role of Chemistry, Biology, Biochemistry, and Bioengineering over these 30 years are discussed. The paper concludes with suggestions for future work.