Electrochemical power text mining using bibliometrics and database tomography

Kostoff, Ronald N.; Tshiteya, Rene; Pfeil, Kirstin M; Humenik, James A.

doi:10.1016/s0378-7753(02)00233-1

Cited by 43 publications

(13 citation statements)

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“…For example, Kostoff has very complete and systematic studies on literature-related analysis, and published a series of papers based on combination of text mining and statistics on scientific papers. Also he proposes a systematic literature-related discovery method for linking two or more literature concepts that have heretofore not been linked, in order to produce novel, interesting, plausible, and intelligible knowledge (Kostoff et al 1998(Kostoff et al , 20022005a, b, 2007a, b, c, d, e, 2008aKostoff 2008a, b, c;Kostoff and Briggs 2008). Ding et al (2001) mapped information retrieval research using co-word analysis on papers collected from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) for the period of 1987-1997.…”

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confidence: 99%

Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight

2010

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This study proposes an approach for visualizing a knowledge structure, the proposed approach creates a three-dimensional ''Research focused parallelship network'', a ''Keyword Co-occurrence Network'', and a two-dimensional knowledge map to facilitate visualization of the knowledge structure created by journal papers from different perspectives. The networks and knowledge maps can be depicted differently by choosing different information as the network actor, e.g. author, institute or country keyword, to reflect knowledge structures in micro-, meso-, and macro-levels, respectively. Technology Foresight is selected as an example to illustrate the method proposed in this study. A total of 556 author keywords contained in 181 Technology Foresight related papers have been analyzed. European countries, China, India and Brazil are located at the core of Technology Foresight research. Quantitative ways of mapping journal papers are investigated in this study to unveil emerging elements as well as to demonstrate dynamics and visualization of knowledge. The quantitative method provided in this paper shows a possible way of visualizing and evaluating knowledge structure; thus a computerized calculation is possible for potential quantitative applications, e.g. R&D resource allocation, research performance evaluation, science map, etc.

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confidence: 99%

Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight

2010

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“…The frequent n-terms phrases are extracted from the orphan document. As shown in figure 7, the frequent phrases, P 1 , P 2 , P 3 …”

Section: Incoming Linksmentioning

confidence: 98%

Link-the-Wiki: Performance Evaluation Based on Frequent Phrases

Chen

Nayak

Geva

2009

Lecture Notes in Computer Science

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“…Other researches focused on bibliometric inputs related with usage y application of the document classification in business and companies [9][10][11][12][13], the learning engine with unbalanced data [14], knowledge management [15][16][17][18][19], Algorithms theory and computational methodologies [20][21][22][23][24][25], computational electronic services [26][27][28], digital information management and context management for smart environments [29][30][31][32][33][34], quality based research and e-assessment [35][36][37][38][39][40], e-business and interpretation techniques [16,[41][42][43][44][45] to identify new concepts, theories, methods and techniques that allow its effective application on hybrid and technological emergent environments [18,[46][47][48][49][50] Nonetheless, there are few researches that allow to obtain or to build a state of the art of particular subject in semiautomatic manner within a time frame. The...…”

Section: Review Of Previous Researchesmentioning

confidence: 99%

State of the Art Construction Based on the J48 Classifier: Case Study of Internet of Things

Anzola

Rojas

Bermúdez

2015

Lecture Notes in Business Information Processing

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Abstract. The bibliometric technics allow us to illustrate aspects of interpretation of data obtained through data bases that are focus in science spreading, which offers an aggregated value to researchers. In this article, we explore the importance of such obtained information from articles' query; in this particular case study the Internet of Things (IoT). The obtained and preprocessed metadata is used to feed the decision tree classification (J48) algorythms, as a result of the training stage, we generated a decision tree/classification where the subjects of research are visualized within a four year time frame. The construction of this classification tree is based in entropy, thus, the order or disorder in which the subjects of research can be interpreted within a level of relevancy in the time frame mentioned. We conceived the visualization and interpretation as the state of the art for the subjects addressed for a particular case, which help researchers to infer tendencies within specific subjects of research.

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Electrochemical power text mining using bibliometrics and database tomography

Cited by 43 publications

References 10 publications

Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight

Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight

Link-the-Wiki: Performance Evaluation Based on Frequent Phrases

State of the Art Construction Based on the J48 Classifier: Case Study of Internet of Things

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