The emergence of care robotics: A publication and patent analysis

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Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between the various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies

Nanotechnology research in Turkey: A university-driven achievement

We deal with nanotechnology research activities in Turkey. Based on publication data retrieved from ISI Web of SSCI database, the main actors and the main characteristics of nanotechnology research in Turkey are identified. Following a brief introduction to nanoscience and nanotechnology research, it goes on with a discussion on nanotechnology related science and technology policy efforts in developing countries and particularly in Turkey. Then using bibliometric methods and social network analysis techniques, this paper aims to understand the main actors of the nanoscale research in Turkey and how they collaborate across institutes and disciplines. The research indicates that there has been an exponential growth in the number of research articles published by Turkish nanoscience and nanotechnology (NST) scholars for the last ten years. However, the analysis of the main characteristics of nanotechnology research carried out at Turkish universities indicates some drawbacks and barriers to the future development of nanotechnology research in Turkey. These barriers are (i) a high concentration of nanoscale research at certain universities; (ii) low level of interdisciplinarity; (iii) a large number of universities which are not well connected to other universities in the field, and finally (iv) low level of international collaborations. Finally, science and technology policy implications of this research are discussed in the conclusion.Emerging technologies nanotechnology, nanoscience, scientific publications, SSCI, bibliometric data, social network analysis, collaborations, interdisciplinarity, science and technology policies, emerging economies, Turkey.