Using network science and text analytics to produce surveys in a scientific topic

Silva, Filipi Nascimento; Amancio, Diego R.; Bardosova, Maria; Costa, Luciano da Fontoura; Oliveira, Osvaldo N.

doi:10.1016/j.joi.2016.03.008

Cited by 123 publications

(116 citation statements)

References 80 publications

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“…In this context, the knowledge space can be represented by a complex network [3,33] where potential discoveries are indicated by nodes, while relationships between such discoveries are represented as edges. Examples of such networks include citation or co-citation networks [7,9,[40][41][42], Wikipedia networks [8,43], and learning objects [44].…”

Section: Knowledge Acquisition Modelingmentioning

confidence: 99%

Knowledge acquisition: A Complex networks approach

Arruda

Silva

Costa

et al. 2017

Information Sciences

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Complex networks have been found to provide a good representation of the structure of knowledge, as understood in terms of discoverable concepts and their relationships. In this context, the discovery process can be modeled as agents walking in a knowledge space. Recent studies proposed more realistic dynamics, including the possibility of agents being influenced by others with higher visibility or by their own memory. However, rather than dealing with these two concepts separately, as previously approached, in this study we propose a multi-agent random walk model for knowledge acquisition that incorporates both concepts. More specifically, we employed the true self avoiding walk alongside a new dynamics based on jumps, in which agents are attracted by the influence of others. That was achieved by using a Lévy flight influenced by a field of attraction emanating from the agents. In order to evaluate our approach, we use a set of network models and two real networks, one generated from Wikipedia and another from the Web of Science. The results were analyzed globally and by regions. In the global analysis, we found that most of the dynamics parameters do not significantly affect the discovery dynamics. The local analysis revealed a substantial difference of performance depending on the network regions where the dynamics are occurring. In particular, the dynamics at the core of networks tend to be more effective. The choice of the dynamics parameters also had no significant impact to the acquisition performance for the considered knowledge networks, even at the local scale.

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Section: Knowledge Acquisition Modelingmentioning

confidence: 99%

Knowledge acquisition: A Complex networks approach

Arruda

Silva

Costa

et al. 2017

Information Sciences

Self Cite

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“…Networks have also been used for knowledge representation in order to visualize differences between the Universal Decimal Classification category structure and that generated by Wikipedia itself [14], to generate automated taxonomies and visualizations of scientific fields [15], and to show connections between articles [16]; furthermore, studies based on the complex networks approach have also been reported [17].…”

Section: Introductionmentioning

confidence: 99%

Science through Wikipedia: A novel representation of open knowledge through co-citation networks

Arroyo-Machado¹,

Torres‐Salinas²,

Herrera‐Viedma³

et al. 2020

PLoS ONE

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This study provides an overview of science from the Wikipedia perspective. A methodology has been established for the analysis of how Wikipedia editors regard science through their references to scientific papers. The method of co-citation has been adapted to this context in order to generate Pathfinder networks (PFNET) that highlight the most relevant scientific journals and categories, and their interactions in order to find out how scientific literature is consumed through this open encyclopaedia. In addition to this, their obsolescence has been studied through Price index. A total of 1 433 457 references available at Altmetric.com have been initially taken into account. After pre-processing and linking them to the data from Elsevier's CiteScore Metrics the sample was reduced to 847 512 references made by 193 802 Wikipedia articles to 598 746 scientific articles belonging to 14 149 journals indexed in Scopus. As highlighted results we found a significative presence of "Medicine" and "Biochemistry, Genetics and Molecular Biology" papers and that the most important journals are multidisciplinary in nature, suggesting also that high-impact factor journals were more likely to be cited. Furthermore, only 13.44% of Wikipedia citations are to Open Access journals.

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“…For example, it has been used to inform policy decisions related to emergency management, financial regulation, and other fields (Ngai & Lee 2016), to understand social media trends and usage (He et al 2013;Mostafa 2013), and in many commercial applications (He et al 2013;Mostafa 2013;Khadjeh Nassirtoussi et al 2014). Silva et al (2016) used such methods, together with graph-theory analyses, to enable mapping and visualizations of scientific fields. These approaches could also be of much use for conservation, for example in identifying knowledge and research gaps (Fisher et al 2010;dos Santos et al 2015;Westgate et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Using machine learning to disentangle homonyms in large text corpora

Roll

Correia

Berger‐Tal

2018

Conservation Biology

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Systematic reviews are an increasingly popular decision-making tool that provides an unbiased summary of evidence to support conservation action. These reviews bridge the gap between researchers and managers by presenting a comprehensive overview of all studies relating to a particular topic and identify specifically where and under which conditions an effect is present. However, several technical challenges can severely hinder the feasibility and applicability of systematic reviews, for example, homonyms (terms that share spelling but differ in meaning). Homonyms add noise to search results and cannot be easily identified or removed. We developed a semiautomated approach that can aid in the classification of homonyms among narratives. We used a combination of automated content analysis and artificial neural networks to quickly and accurately sift through large corpora of academic texts and classify them to distinct topics. As an example, we explored the use of the word reintroduction in academic texts. Reintroduction is used within the conservation context to indicate the release of organisms to their former native habitat; however, a Web of Science search for this word returned thousands of publications in which the term has other meanings and contexts. Using our method, we automatically classified a sample of 3000 of these publications with over 99% accuracy, relative to a manual classification. Our approach can be used easily with other homonyms and can greatly facilitate systematic reviews or similar work in which homonyms hinder the harnessing of large text corpora. Beyond homonyms we see great promise in combining automated content analysis and machine-learning methods to handle and screen big data for relevant information in conservation science.

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Using network science and text analytics to produce surveys in a scientific topic

Cited by 123 publications

References 80 publications

Knowledge acquisition: A Complex networks approach

Knowledge acquisition: A Complex networks approach

Science through Wikipedia: A novel representation of open knowledge through co-citation networks

Using machine learning to disentangle homonyms in large text corpora

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