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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

This work tries to propose a set of evaluation dimensions for the analysis of the knowledge organization systems (KOS), building over previous research and the available literature on the subject. It presents a compiled taxonomy of KOSs, a set of tentative characteristics proposed in the literature and the authors' spectra proposal. The full details of the typology are not covered in the scope of the article, but will be available as an ontology in the near future.

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Abstract

This paper analyzes previous work on the classification of Knowledge Organization Systems (KOS), discusses strengths and weaknesses, and proposes a new and integrative framework. It argues that current analyses of the KOS tend to be idiosyncratic and incomplete, relying on a limited number of dimensions of analysis. The paper discusses why and how KOS should be classified on a new basis. Based on the available literature and previous work, the authors propose a wider set of dimensions for the analysis of KOS. These are represented in a taxonomy of KOS. Issues arising are discussed.

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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