Social classification is the process in which a community of users categorizes the resources in that community for their own use. Given enough users and categorization, this will lead to any given resource being represented by a set of labels or descriptors shared throughout the community (Mathes, 2004). Social classification has become an extremely popular way of structuring online communities in recent years. Well-known examples of such communities are the bookmarking websites Furl (http://www.furl.net/) and del.icio.us (http://del.icio.us/), and Flickr (http://www.flickr.com/) where users can post their own photos and tag them. Social classification, however, is not limited to tagging resources: another possibility is to tag people, examples of which are Consumating (http://www.consumating.com/), a collaborative tag-based personals website, and Kevo (http://www.kevo.com/), a website that lets users tag and contribute media and information on celebrities. Another application of people tagging is expertise classification, an emerging subfield of social classification. Here, members of a group or community are classified and ranked based on the expertise they possess on a particular topic. Expertise classification is essentially comprised of two different components: expertise tagging and expert ranking. Expertise tagging focuses on describing one person at a time by assigning tags that capture that person's topical expertise, such as â speech recognition' or â small-world networks'. information request, such as, for instance, a query submitted to a search engine. Methods are developed to combine the information about individual members' expertise (tags), to provide on-the-fly query-driven rankings of community members. Expertise classification can be done in two principal ways. The simplest option follows the principle of social bookmarking websites: members are asked to supply tags that describe their own expertise and to rank the other community members with regard to a specific request for information. Alternatively, automatic expertise classification ideally extracts expertise terms automatically from a user's documents and e-mails by looking for terms that are representative for that user. These terms are then matched on the information request to produce an expert ranking of all community members. In this paper we describe such an automatic method of expertise classification and evaluate it using human expertise classification judgments. In the next section we will describe some of the related work on expertise classification, after which we will describe our automatic method of expertise classification and our evaluation of them in sections 3 and 4. Sections 5.1 and 5.1 describe our findings on expertise tagging and expert rankings, followed by discussion and our conclusions in section 6 and recommendations for future work in section 7.

There is increasing evidence that libraries, traditional and digital, must support learning, especially the acquisition and enhancement of scientific reasoning skills. This paper discusses how classificatory structures, such as a faceted thesaurus, can be enhancedfor novice science learning. Physical geography is used as the domain discipline, and the Alexandria Digital Earth Prototype project provides the test bed for instructional materials and user analyses. The use of concept maps and topic maps for developing digital learning spaces is briefly discussed.

The authors use an exploratory project involving Web resources related to Alzheimer’s Disease to explore ways in RDF metadata can more effectively translate the virtues of the traditional vertical file to a Web environment form using Semantic Web descriptive standards. In so doing, they argue against the separation of “bibliographic control” from the socially-embedded institutional practices of reference work, collection development, and the management of information ephemera. Libraries of the future will use specific Web technologies that lend themselves to sophisticated and rigorous knowledge structures, and link them with librarians’ skills in information harvesting and evaluation.

Theorists have suggested that organizational culture is a strategic resource that has value in ensuring the continuing existence and success of organizations (Michalisin, Smith, & Kline, 1997; Barney, 1986, 1991; Hult, Ketchen, & Nichols, 2002; Gordon, 1985). This assertion is supported by various studies that have linked organizational culture to broad strategic outcomes such as an organizationâ s ability to manage knowledge (Davenport, Long, & Beers, 1998; Storck & Hill, 2000), innovation capability (Hauser, 1998), and strategic management of information technology (Kaarst-Brown & Robey, 1999; Reich & Benbasat, 2000; Schein, 1985). Based on this research, we suggest that there are characteristics of organizational cultures in information-based organizations that lead to increased collaboration, collegiality, and organizational effectiveness.

The purpose of this research is to improve retrieval performance in systems that use assigned subject descriptors, such as library subject headings. We are looking for wider semantic boundaries surrounding summary headings assigned to documents by providing a means of identifying clustered headings that fall within the indexerâ s collective common perceptions of relevance. We are here experimenting with two techniques that can help increase both precision and recall. In earlier research citationâ chasing was employed to yield a fuller retrieval set than might have been found using subject headings alone. In the present study we are employing multiâ dimensional scaling to determine the best fit among works to which subject descriptors have been coâ assigned. A term co-occurrence matrix compiled from 19 LCSH subject headings assigned to works in the field of â language originâ is used to generate an MDS map of the semantic space. Two clusters emerge: language and languages, and evolution biology, sometimes termed evolingo. Results allow us to visualize how differing perceptions of indexers affect the semantic space surrounding assigned terms. In both cases - citation-chasing and term co-occurrence - and especially when combining the two techniques acting as thresholds for each other, it is possible to overcome the inverse relation between precision and recall.

As one important experiment in the social or user-generated classification of online cultural heritage resources collections, art museums are leading the effort to elicit keyword descriptions of artwork images from online museum visitors. The motivations for having online viewers - presumably largely non-art-specialists - describe art images are (a) to generate keywords for image and object records in museum information retrieval systems in a cost-effective way and (b) to engage online visitors with the artworks and with each other by inviting visitors to express themselves and share their descriptions of artworks. This paper explores the question of how effective non-specialist art keyworders can be in capturing ("tagging") potentially useful concepts and terms for use in art information retrieval systems. To do this, the paper compares evidence from art museum visitor studies which describe how non-specialist art viewers react to and describe artworks and use museum-supplied information in their initial encounters with artworks. A theoretical model of artwork interpretation derived from art museum visitor research provides a framework with which to examine both the activity and the products of artwork tagging for image and information retrieval.

This is a preliminary scan of S.R. Ranganathan's Philosophy of Library Classification (1989 Indian reprint edition). Contents include: Conspectus, 8 chapters, and Index: 1. Evolution of classification. 2. Library classification: an artificial language. 3. Library Classification and social forces. 4. Library classification as a transformation and its limitations. 5. Library classification and its symbiosis with library catalogue. 6. Capacity of library classification. 7. Increasing of dimensions and optional facets. 8. Organisation for research in library classification. © Sarada Ranganathan Endowment for Library Science (SRELS). This is a title in the dLIST Classics project; permission for non-profit use granted by SRELS. To purchase print reprints of this work, please visit Ess Ess Publications at http://www.essessreference.com/.

Knowledge organization and concepts, knowledge organization as information policy, groupings as discoveries and constructions, semantics and its "warrant", structural linguistics, compositional semantics and semantic primitives, and universal solutions along with limitations are discussed in a series of 42 slides. "The most important conclusion, ... is that different "paradigms" or conceptualizations in the discourses should be made visible and semantic tools should support the navigation not just between "topics" but also between "views" and interests. If such information is made explicit it might improve the possibilities for internationalization because nobody's vital interests are then threatened." A reference list (of seven references) is included.

Nanoscience and technology is characterized by nano researchers as an increasingly interdisciplinary domain, drawing upon such disciplines as chemistry, physics, materials science, and computer, electrical, mechanical and biomedical engineering. A key challenge faced by information professionals involved in organizing and providing the related information services is to efficiently identify information resources and to carry out inclusive and effective searches in a diverse and heterogeneous range of digital libraries, web-based databases and search engines. This demand emphasizes the importance of thinking about and developing methodological models for investigating interdisciplinary knowledge organization practices. This 2008 study examined the extent of interdisciplinarity in user queries submitted to the NANOnetBASE digital library. Transaction logs of the digital library were analyzed to explore usersâ search behaviour patterns and to examine the extent to which user queries were interdisciplinary. The Inspect thesaurus and Classification codes were utilized the disciplinary or interdisciplinary focus of the queries. The results indicate that 62% of the unique top terms resulting from mapping usersâ query terms to the INSPEC Classification codes represented two or more disciplines, specifically terms associated with the Classification code â Aâ representing â physics.â The results contribute to the development of more critical information organization and classification practices in such an increasingly interdisciplinary domain as nanoscience and technology.

This is a presentation to the Strong Start program (new MLS students) at the University of Arizona on 19 August 2002. There are 32 slides. Contents: Definitions, Assumptions, Limitations; What are Information Environments? The Roles We Play; Knowledge Organization (KO); The Cataloging/Metadata Process; Metadata â standards, types, initiatives; Q & A; Discussion; Resources. Interestingly, dLIST is mentioned and volunteers solicited!