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Digital heritage portal interfaces are generally similar to digital library and search engine interfaces in displaying search results as a list of brief metadata records. The knowledge organization and search result display of these systems are item-centric, with little support for identifying relationships between items. This paper proposes a knowledge graph system and visualization interface as a promising solution for digital heritage systems to support users in browsing related items, understanding the relationships between items, and synthesizing a narrative on an issue. The paper discusses design issues for the knowledge graph, graph database, and graph visualization, and offers recommendations based on the authors' experience in developing three knowledge graph systems for archive and digital humanities resources: the Zubir Said personal archive collection at the Nanyang Academy of Fine Arts, Singapore; Singapore Pioneers social network; and Polyglot Medicine knowledge graph of Asian traditional and herbal medicine. Lessons learned from a small user study are incorporated in the discussion. [ABSTRACT FROM AUTHOR]

Introduction: This is a study of hierarchical navigation; how users browse a taxonomy-based interface to an organizational repository to locate information resources. The study is part of a project to develop a taxonomy for an library and information science department to organize resources and support user browsing in a digital repository. Method: The data collection was carried out using task-based navigation exercises with twenty-two participants. A cognitive framework of hierarchical navigation is proposed, involving the cognitive process of matching context, topic and/or resource type concepts to taxonomy categories. Analysis: The analysis was mainly qualitative, supplemented with simple statistics and measures of prevision and recall, and error analysis. Results: Though users often use the topic concept in making navigation choices, they sometimes make use of context and resource-type concepts. Users infer a variety of relationships between a task concept and a taxonomy category, including the application area, associated tool, associated process/procedure/technique, associated institution and academic discipline. Conclusions: Users prefer to use common or generic associations in selecting categories to browse, rather than formal disciplinary relations. Some users prefer to search by people groups, contexts and institutions, rather than by subject categories. Users have difficulty distinguishing between various kinds of document and resource types. (Contains 5 tables.)

Proposes a variable-based framework for multi-document summarization of dissertation abstracts in the fields of sociology and psychology that makes use of the macro- and micro-level discourse structure of dissertation abstracts as well as cross-document structure. Provides a list of indicator phrases that denote different aspects of the problem statements. (Author/AEF)

Describes the development of new statistical formulas for identifying two- and three-character words in Chinese text by performing stepwise logistic regression using a sample of sentences that had been manually segmented. Concludes that the new contextual information formulas are substantially better than the mutual information formula. (Author/LRW)

This study used semantic relations expressed in text, particularly cause-effect relations, to improve information retrieval effectiveness. Investigated whether information obtained by matching cause-effect relations expressed in documents with the cause-effect relations expressed in users' queries can be used to improve retrieval results, in comparison to using just keyword matching. (Author/LRW)

Copyright of Iberica is the property of Asociacion Europea de Lenguas para Fines Especificos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reviews methods to improve online catalogs for subject searching and describes the design of an expert system front-end to improve subject access in online public access catalogs that focuses on search strategies. Implementation of a prototype system at the National University of Singapore is described, and reformulation strategies are discussed. (Contains 25 references.) (LRW)

When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause–effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause–effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified—showing how comparisons are used to support the claim that the cause‐effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing. [ABSTRACT FROM AUTHOR]

When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause–effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause–effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified—showing how comparisons are used to support the claim that the cause‐effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing. [ABSTRACT FROM AUTHOR]

Copyright of Iberica is the property of Asociacion Europea de Lenguas para Fines Especificos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, Multi-perspective Question Answering (MPQA) Subjectivity Lexicon, General Inquirer, National Research Council Canada (NRC) Word-Sentiment Association Lexicon and Semantic Orientation Calculator (SO-CAL) lexicon. The effectiveness of the sentiment lexicons for sentiment categorisation at the document level and sentence level was evaluated using an Amazon product review data set and a news headlines data set. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorisation, obtaining accuracy rates of 75%–77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words for both document-level and sentence-level sentiment categorisation. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorisation task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human-assigned sentiment values. It is recommended that the Hu & Liu lexicon be used for product review texts and the WKWSCI lexicon for non-review texts. [ABSTRACT FROM AUTHOR]