CODE4: Applications for Managing Classification Schemes

Studies in Classification, Data Analysis, and Knowledge Organization, 2010

This volume contains revised selected papers from plenary and invited as well as contributed sessions at the 11th Biennial Conference of the International Federation of Classification Societies (IFCS) in combination with the 33rd Annual Conference of the German Classification Society-Gesellschaft für Klassifikation (GfKl), organized by the Faculty of Business Management and Economics at the Technische Universität Dresden in March 2009. The theme of the conference was "Classification as a Tool for Research." The conference encompassed 290 presentations in 100 sessions, including 11 plenary talks and 2 workshops. Moreover, five tutorials took place before the conference. With 357 attendees from 58 countries, the conference provided a very attractive interdisciplinary international forum for discussion and mutual exchange of knowledge. The chapters in this volume were selected in a second reviewing process after the conference. From the remaining 120 submitted papers, 90 papers were accepted for this volume. In addition to the fundamental methodological areas of Classification and Data Analysis, the volume contains many chapters from a wide range of topics representing typical applications of classification and data analysis methods in Archaeology and Spatial Science, Bio-Sciences, Electronic Data and Web, Finance and Banking, Linguistics, Marketing, Music Science, and Quality Assurance and Engineering. The editors would like to thank the session organizers for supporting the spread of information about the conference, and for inviting speakers, all reviewers for their timely reports, and Irene Barrios-Kezic and Martina Bihn of Springer-Verlag, Heidelberg, for their support and dedication to the production of this volume. Moreover, IFCS and GfKl want to thank the Local Organizing Committee,

Bulletin of the American Society for Information Science and Technology, 2012

This paper analyses the literature of classification published during 2000 to 2009 and finds that there is sustainability in the growth of literature on classification in the first decade of the 21st century. It traces the pattern in scattering of literature on classification in library and information science (LIS) journals and concludes that the literature adheres to the Bradford’s law of scattering. It produces rank list of journals publishing the literature on classification and identifies authorship patterns and the prominent writers in classification. The research finds that the Indian LIS writers have shown sustained interest in classification domain.

2001

The members of SIG/CR have been challenged to consider seriously what lies ahead in knowledge organization. To plan for the future, it is important to understand the past. This paper provides a brief overview of problems in classification research from 1957 until 1964 as well as insights into the creation ofthe Classification Research Study Group (CRSG), which formed the nucleus of SIG/CR in 1970.

Journal of the American Society for Information …, 2007

The field of Information Science is constantly changing. Therefore, information scientists are required to regu-larly review—and if necessary—redefine its fundamental building blocks. This article is one of a group of four articles, which resulted from a Critical Delphi study ...

Software & Systems Modeling, 2016

Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the “one and the many.” Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor’s work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus to the ISE literature. The literature survey follows the evolution of ISE’s understanding of how to formalize the classification pattern. The various proposals are assessed using the classical example of classification; the Linnaean taxonomy formalized using powersets as a benchmark for formal expressiveness. The broad conclusion of the survey is that (1) the ISE community is currently in the early stages of the process of understanding how to formalize the classification pattern, particularly in the requirements for expressiveness exemplified by powersets, and (2) that there is an opportunity to intervene and speed up the process of adoption by clarifying this expressiveness. Given the central place that the classification pattern has in domain modeling, this intervention has the potential to lead to significant improvements.

AbsTrACT Knowledge organisation is a sub-discipline of Information Studies and has its roots in Philosophy. Being application-oriented, it is an area of major interest to librarians, webpage designers, information architects, and semantic web community. The paper examines the scope of 'knowledge organisation' and its various facets. The different approaches to knowledge organisation are examined and the requirements in the context of digital environment are highlighted. An overview of the major trends and approaches is provided.

Schmidt, K.; Simone, C.; Star, SL: Workshop Classification Schemes, 2000

Categorisation is an inherent feature of much individual and collective work. Filing, coding, sorting, collating and comparing inevitably rely on schemes by which items can be categorised. Technical systems introduce a range of mechanisms for categorisation, from simple file systems to intricate database technologies, and these systems are deployed into settings in which categorisation work is done.

Encyclopedia of Data Warehousing and Mining, Second Edition

Generally speaking, classification is the action of assigning an object to a category according to the characteristics of the object. In data mining, classification refers to the task of analyzing a set of pre-classified data objects to learn a model (or a function) that can be used to classify an unseen data object into one of several predefined classes. A data object, referred to as an example, is described by a set of attributes or variables. One of the attributes describes the class that an example belongs to and is thus called the class attribute or class variable. Other attributes are often called independent or predictor attributes (or variables). The set of examples used to learn the classification model is called the training data set. Tasks related to classification include regression, which builds a model from training data to predict numerical values, and clustering, which groups examples to form categories. Classification belongs to the category of supervised learning, ...

2003

This paper discusses issues and s olutions for supporting multiple overlapping classifications in database systems. These classifications are commonly found in science, although they are often ignored in computing applications for scientific data, and inappropriate solutions adopted as their replacement. Known database models and classification techniques offer some degree of support for multiple overlapping classifications, but do not fully support the basic features we have identified as necessary: trees/graphs, traceability, semantics of classifications, independence of classification and data, and identity of classifications. The approach to the problem adopted by the Prometheus project, based on an extended object-oriented database model and the independence of classification schemes from classified data, is presented and discussed.