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Abstract

The main objective of this research was to analyze whether there was a characteristic distribution behavior of relevant terms over a scientific text that could contribute as a criterion for their process of automatic indexing. The terms considered in this study were only full noun phrases contained in the texts themselves. The texts were considered a total of 98 doctoral theses of the eight areas of knowledge in a same university. Initially, 20 full noun phrases were automatically extracted from each text as candidates to be the most relevant terms, and each author of each text assigned a relevance value 0-6 (not relevant and highly relevant, respectively) for each of the 20 noun phrases sent. Only, 22.1 % of noun phrases were considered not relevant. A relevance values of the terms assigned by the authors were associated with their positions in the text. Each full noun phrases found in the text was considered as a valid linear position. The results that were obtained showed values resulting from this distribution by considering two types of position: linear, with values consolidated into ten equal consecutive parts; and structural, considering parts of the text (such as introduction, development and conclusion). As a result of considerable importance, all areas of knowledge related to the Natural Sciences showed a characteristic behavior in the distribution of relevant terms, as well as all areas of knowledge related to Social Sciences showed the same characteristic behavior of distribution, but distinct from the Natural Sciences. The difference of the distribution behavior between the Natural and Social Sciences can be clearly visualized through graphs. All behaviors, including the general behavior of all areas of knowledge together, were characterized in polynomial equations and can be applied in future as criteria for automatic indexing. Until the present date this work has become inedited of for two reasons: to present a method for characterizing the distribution of relevant terms in a scientific text, and also, through this method, pointing out a quantitative trait difference between the Natural and Social Sciences.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Abstract

In this paper we discuss, under a critical point of view, the current Information Science landscape and some future prospects regarding contemporary information phenomena. We present thoughts about the process of thematic deflation of Information Science, through the analysis of the research objects currently under development in this field. In addition to this, we look at the process of absorption of these and other relevant objects in distinguished knowledge fields. We seek to challenge the emphasis and the volume of interdisciplinary research within the field, and present some comments about what might be the results of such processes for the future of Information Science. Subsequently, we analyze the impact in the Information Science field due to phenomena like information boom, the consolidation of the social networks as interactive spaces, cloud computing, as well as other key elements.

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Abstract

Presents the semantic portal project of the center for teaching and research in the Social Sciences and Contemporary History (CPDOC) of the Fundação Getúlio Vargas, Rio de Janeiro. This project involves the use of semantic and visualization technologies and natural language processing techniques to allow enhanced ways to access the CPDOC collections.

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Abstract

Knowledge organization (KO) as an activity is, among other meanings, a process for conceptual modeling of knowledge domains that produces a consensual abstraction model of this domain with a particular purpose. It adopts a myriad of techniques to analyze and build efficient knowledge organization systems, and one of these techniques is called sentiment analysis (SA) or opinion mining, which is emerging as promising and useful in a variety of ways. It is based in NLP and AI algorithms, and aims at identifying opinions and emotions toward any person, organization or subject; evaluating them as positive or negative, in both binary and graded fashions. This study sought to show various aspects of the implementation of SA for knowledge organization tasks as register ed in the scientific literature. We began with exploratory bibliographic research and built a corpus of 91 scientific papers, written in English, selected in the LISA Database, between 2000 to 2016. We analyzed these papers and extracted title, year of publication, author(s) and institution(s), title of the journal where they were published, keywords, the LISA classification code, methods/techniques adopted and its application areas. Our main findings are that theoretical papers still prevail, which may indicate a field in the early stages. We found many institutions and authors from Asia, which points to a new shift in world expertise. We concluded that SA is still a novelty in the KO field, being slowly adopted as an aid to the main tasks, as document classification.

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Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik