Text mining using database tomography and bibliometrics: A review

Kostoff, Ronald N.; Toothman, Darrell Ray; Eberhart, Henry J.; Humenik, James A.

doi:10.1016/s0040-1625(01)00133-0

Cited by 127 publications

(56 citation statements)

References 14 publications

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“…Bibliometrics help examine, organise and analyse large amounts of historical data and help analysts identify "hidden patterns" that may help researchers in the process of decision-making. There are several important works (Morris, 2002;Kostoff et al, 2001;Feng et al, 2015), where the authors, using information from databases and system analysis in the form of DIVA software, database tomography or CiteSpace, visualise documents as clusters on the bidimensional map and thus have the opportunity to study interrelations between documents and clusters of documents, to formulate summary data on each cluster of documents and, correspondingly, to give an idea about the trends and projections in the field of related scientific interests.…”

Section: Methodology and Datamentioning

confidence: 99%

University–Industry Interaction Trends in the Baltic Sea Region: A Bibliometric Analysis

Мурашова

Логинова

2017

Baltic Journal of European Studies

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Section: Methodology and Datamentioning

confidence: 99%

University–Industry Interaction Trends in the Baltic Sea Region: A Bibliometric Analysis

Мурашова

Логинова

2017

Baltic Journal of European Studies

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“…Interesting examples include visualizing the inter-relationships between research topics (Porter 2005, Small 2006, identification of important researchers or research groups (Kostoff 2001, Losiewicz et al 2000, the study of research performance by country (de Miranda et al 2006), (Kim and Mee-Jean 2007), the study of collaboration patterns (Anuradha et al 2007,Chiu and Ho 2007, Braun et al 2000 and the prediction of future trends and developments (Smalheiser 2001, Daim et al 2005, Daim et al 2006, Small 2006.…”

Section: Novelty and Motivationmentioning

confidence: 99%

Measuring Innovation Using Bibliometric Techniques: The Case of Solar Photovoltaic Industry

Vidican

Woon²,

Madnick

2009

SSRN Journal

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In this paper, we use feature extraction and data analysis techniques for the elucidation of patterns and trends in technological innovation. In studying innovation, we focus on the role of public research institutions (research universities and national laboratories) in the development of new industries. More specifically, we are interested in measuring innovation through research collaborations between these institutions and the private sector.The proposed methods are primarily drawn from the field of bibliometrics -i.e. the analysis of information and trends in the publication of text documents, rather than the contents of these documents. In particular, we seek to explore the relationship between joint publication patterns and trends, R&D funding, technology development choices, and the viability and effectiveness of industry-university collaborations.To focus the discussions and to provide concrete examples of their applicability, this study will have an initial emphasis on the solar photovoltaic (PV) sector in the U.S., though the techniques and general approach devised here will be applicable to a broad range of industries, situations, and locations.Our analysis suggests that interesting information and conclusions can be derived from this line of analysis. The results obtained using our data extraction techniques allow us to identify early technology focus in different areas within solar PV technologies, and to determine potential technology pathways, which is critical for innovation policy in the renewable energy domain.2

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“…There is already a significant body of research addressing this problem (for a good review, the reader is referred to [Porter, 2005, Porter, 2007, Losiewicz et al, 2000, Martino, 1993); interesting examples include visualizing the inter-relationships between research topics [Porter, 2005, Small, 2006, identification of important researchers or research groups [Kostoff, 2001, Losiewicz et al, 2000, the study of research performance by country [de Miranda et al, 2006, Kim andMee-Jean, 2007], the study of collaboration patterns [Anuradha et al, 2007, Chiu and Ho, 2007, Braun et al, 2000 and the prediction of future trends and developments [Smallheiser, 2001, Daim et al, 2005, Daim et al, 2006, Small, 2006. Nevertheless, given the many difficulties inherent to these undertakings, there is still much scope for further development in many of these areas.…”

Section: Technology Miningmentioning

confidence: 99%

Latent Semantic Analysis Applied to Tech Mining

Ziegler¹,

Woon

Madnick

2009

SSRN Journal

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-This paper presents an approach to bibliometric analysis in the context of technology mining. Bibliometric analysis refers to the use of publication database statistics, e.g., hit counts relevant to a topic of interest. Technology mining facilitates the identification of a technology's research landscape. Our contribution to bibliometrics in this context is the use of a technique known as Latent Semantic Analysis (LSA) to reveal the concepts that underlie the terms relevant to a field. Using this technique, we can analyze coherent concepts, rather than individual terms. This can lead to more useful results from our bibliometric analysis. We present results that demonstrate the ability of Latent Semantic Analysis to uncover the concepts underlying sets of key terms, used in a case study on the technologies of renewable energy.

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Text mining using database tomography and bibliometrics: A review

Cited by 127 publications

References 14 publications

University–Industry Interaction Trends in the Baltic Sea Region: A Bibliometric Analysis

University–Industry Interaction Trends in the Baltic Sea Region: A Bibliometric Analysis

Measuring Innovation Using Bibliometric Techniques: The Case of Solar Photovoltaic Industry

Latent Semantic Analysis Applied to Tech Mining

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