Search (10 results, page 1 of 1)

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Abstract

Reports results of a study of 4 WWW search engines: AltaVista; Lycos; Excite and WebCrawler to collect data on Web objects on polymer chemistry. 1.037 Web objects were examined for data in 4 categories: document information; use of meta fields; use of images and use of chemical names. Issues raised included: whether to provide metadata elements for parts of entities or whole entities only, the use of metasyntax, problems in representation of special types of objects, and whether links should be considered when encoding metadata. Use of metafields was not widespread in the sample and knowledge of metafields in HTML varied greatly among Web object creators. The study formed part of a metadata project funded by the OCLC Library and Information Science Research Grant Program

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Source

Challenges and opportunities for knowledge organization in the digital age: proceedings of the Fifteenth International ISKO Conference, 9-11 July 2018, Porto, Portugal / organized by: International Society for Knowledge Organization (ISKO), ISKO Spain and Portugal Chapter, University of Porto - Faculty of Arts and Humanities, Research Centre in Communication, Information and Digital Culture (CIC.digital) - Porto. Eds.: F. Ribeiro u. M.E. Cerveira

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Abstract

The different points of views an knowledge representation and organization from various research communities reflect underlying philosophies and paradigms in these communities. This paper reviews differences and relations in knowledge representation and organization and generalizes four paradigms-integrative and disintegrative pragmatism and integrative and disintegrative epistemologism. Examples such as classification, XML schemas, and ontologies are compared based an how they specify concepts, build data models, and encode knowledge organization structures. 1. Introduction Knowledge representation (KR) is a term that several research communities use to refer to somewhat different aspects of the same research area. The artificial intelligence (AI) community considers KR as simply "something to do with writing down, in some language or communications medium, descriptions or pictures that correspond in some salient way to the world or a state of the world" (Duce & Ringland, 1988, p. 3). It emphasizes the ways in which knowledge can be encoded in a computer program (Bench-Capon, 1990). For the library and information science (LIS) community, KR is literally the synonym of knowledge organization, i.e., KR is referred to as the process of organizing knowledge into classifications, thesauri, or subject heading lists. KR has another meaning in LIS: it "encompasses every type and method of indexing, abstracting, cataloguing, classification, records management, bibliography and the creation of textual or bibliographic databases for information retrieval" (Anderson, 1996, p. 336). Adding the social dimension to knowledge organization, Hjoerland (1997) states that knowledge is a part of human activities and tied to the division of labor in society, which should be the primary organization of knowledge. Knowledge organization in LIS is secondary or derived, because knowledge is organized in learned institutions and publications. These different points of views an KR suggest that an essential difference in the understanding of KR between both AI and LIS lies in the source of representationwhether KR targets human activities or derivatives (knowledge produced) from human activities. This difference also decides their difference in purpose-in AI KR is mainly computer-application oriented or pragmatic and the result of representation is used to support decisions an human activities, while in LIS KR is conceptually oriented or abstract and the result of representation is used for access to derivatives from human activities.

Date

12. 9.2004 17:22:35

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Source

Library trends. 48(1999) no.1, S.109-132

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Date

24. 8.2005 19:20:22

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Abstract

It is important for education in computer science and information systems to keep up to date with the latest development in technology. With the rapid development of the Internet and the Web, many schools have included Internet-related technologies, such as Web search engines and e-commerce, as part of their curricula. Previous research has shown that it is effective to use search engine development tools to facilitate students' learning. However, the effectiveness of these tools in the classroom has not been evaluated. In this article, we review the design of three search engine development tools, SpidersRUs, Greenstone, and Alkaline, followed by an evaluation study that compared the three tools in the classroom. In the study, 33 students were divided into 13 groups and each group used the three tools to develop three independent search engines in a class project. Our evaluation results showed that SpidersRUs performed better than the two other tools in overall satisfaction and the level of knowledge gained in their learning experience when using the tools for a class project on Internet applications development.

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Abstract

The structural, functional, and production views on learning objects influence metadata structure and vocabulary. The authors drew on these views and conducted a literature review and in-depth analysis of 14 learning objects and over 500 components in these learning objects to model the knowledge framework for a learning object ontology. The learning object ontology reported in this article consists of 8 top-level classes, 28 classes at the second level, and 34 at the third level. Except class Learning object, all other classes have the three properties of preferred term, related term, and synonym. To validate the ontology, we conducted a query log analysis that focused an discovering what terms users have used at both conceptual and word levels. The findings show that the main classes in the ontology are either conceptually or linguistically similar to the top terms in the query log data. The authors built an "Exercise Editor" as an informal experiment to test its adoption ability in authoring tools. The main contribution of this project is in the framework for the learning object domain and the methodology used to develop and validate an ontology.

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Abstract

Different research fields have different definitions for semantic patterns. For knowledge discovery and representation, semantic patterns represent the distribution of occurrences of words in documents and/or citations. In the broadest sense, the term semantic patterns may also refer to the distribution of occurrences of subjects or topics as reflected in documents. The semantic pattern in a set of documents or a group of topics therefore implies quantitative indicators that describe the subject characteristics of the documents being examined. These characteristics are often described by frequencies of keyword occurrences, number of co-occurred keywords, occurrences of coword, and number of cocitations. There are many ways to analyze and derive semantic patterns in documents and citations. A typical example is text mining in full-text documents, a research topic that studies how to extract useful associations and patterns through clustering, categorizing, and summarizing words in texts. One unique way in library and information science is to discover semantic patterns through bibliographically coupled citations. The history of bibliographical coupling goes back in the early 1960s when Kassler investigated associations among technical reports and technical information flow patterns. A number of definitions may facilitate our understanding of bibliographic coupling: (1) bibliographic coupling determines meaningful relations between papers by a study of each paper's bibliography; (2) a unit of coupling is the functional bond between papers when they share a single reference item; (3) coupling strength shows the order of combinations of units of coupling into a graded scale between groups of papers; and (4) a coupling criterion is the way by which the coupling units are combined between two or more papers. Kessler's classic paper an bibliographic coupling between scientific papers proposes the following two graded criteria: Criterion A: A number of papers constitute a related group GA if each member of the group has at least one coupling unit to a given test paper P0. The coupling strength between P0 and any member of GA is measured by the number of coupling units n between them. G(subA)(supn) is that portion of GA that is linked to P0 through n coupling units; Criterion B: A number of papers constitute a related group GB if each member of the group has at least one coupling unit to every other member of the group.

Source

Encyclopedia of library and information science. Vol.72, [=Suppl.35]

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Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas