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Abstract

The construction of concepts achieved by the apparently incompatible ideas of essence and context is examined through genre. Essence is defined by essential characteristics: innate, immutable, independent of context. Unlike essences, contexts are fluid, changing with time and location. Genre has the stability of the essential characteristics that define essence and the fluidity of differing circumstances that define context, thus making it effective for the exploration of essence and context. Controlled vocabularies reveal diachronically and synchronically the stable/fluid ambivalence of genre classes. The Dewey Decimal Classification (DDC1, DDC13, DDC23) exhibits stability (and modest fluidity) in the Divisions, the primary reflection of academic disciplines one hierarchical step below the main classes and the development of the standard subdivisions as a slow multi-edition evolution. Genre serves as a lens for us to better understand essences, contexts, and concepts and their manifestations, classes. Rather than being incompatible opposites, essences and contexts complement each other in the definition of concepts. How these abstractions relate to classification is a question both theoretical and practical to our efforts to further knowledge organization.

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Abstract

Few studies have been done concerning document components and their effects on information use. This research empirically tested a taxonomy of functional units in a prototype journal system. This taxonomy was developed by identifying functions of the smallest information units within four journal article components (i.e., introduction, methods, results, discussion), and their associations with information tasks of using journal articles. Experimental results show that functional units can be utilized in supporting navigation, close reading, comprehension, and information use of journal articles to various extents. The results provide evidence that an individual functional unit has varying relevance to information use tasks, and has varying relevance to other functional units in the same or another component for a particular task. This research suggests that the information within a journal article can be organized and presented by functions to enhance effectiveness and efficiency in reading process and reading outcome.

Date

6. 4.2012 18:43:22

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Abstract

Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model.

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Date

25. 6.2013 20:29:05

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Date

29. 5.2015 19:23:26