Establishing the Value of Socially Created Metadata to Image Indexing
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Thesauruses and Ontologies
Thesauruses and ontologies Silvia Arano Citación recomendada: Silvia Arano. Thesauruses and ontologies [en linea]. "Hipertext.net", num. 3, 2005. <http://www.hipertext.net> [Consulted: 12 feb. 2007]. 1. Introduction During the past few years, the information representation and retrieval sector in the area of Documentation and Biblioteconomy has had to assume the important repercussions of the Internet and its associated technologies, and in particular, the World Wide Web (WWW). Technological modifications arising from these important changes are leading to the gradual digitalisation of the information representation and retrieval sector, affecting information artefacts, representation and retrieval tools and user requirements. In the light of this growing context of digitalisation, diverse information representation and retrieval tools exist, which must be studied in addition to diverse fields of knowledge in which these tools have originated: Linguistics, Artificial Intelligence, Documentation, Linguistic Engineering... Hence, in specialised literature, analyses are performed on information representation and retrieval tools, taxonomies, classification systems, computational lexicons, lexical databases, thesauruses, titles lists, knowledge bases, conceptual maps, ontologies, synonym rings and semantic networks, among others. Among this wide spectrum of information representation and retrieval tools are thesauruses and ontologies, which are most often linked in bibliography, even though they come from completely different disciplinary areas. However, the conceptualisation applied by authors to the terms "thesaurus" and "ontology" is quite diverse, and sometimes authors confuse, oppose, complement or overlap both these concepts. The overall objective of the present article [ 1] is to establish the relationship between the concepts of thesaurus and ontology in the Documentation and Biblioteconomy field. Two specific objectives have been established for this purpose. -
OGC Testbed-14: Semantically Enabled Aviation Data Models Engineering Report
OGC Testbed-14 Semantically Enabled Aviation Data Models Engineering Report Table of Contents 1. Summary . 4 1.1. Requirements & Research Motivation . 4 1.2. Prior-After Comparison. 4 1.3. Recommendations for Future Work . 5 1.4. What does this ER mean for the Working Group and OGC in general . 6 1.5. Document contributor contact points . 6 1.6. Foreword . 6 2. References . 8 3. Terms and definitions . 9 3.1. Semantics . 9 3.2. Service Description. 9 3.3. Service-Oriented Architecture (SOA) . 9 3.4. Registry . 9 3.5. System Wide Information Management (SWIM) . 9 3.6. Taxonomy . 9 3.7. Web Service . 10 4. Abbreviated Terms . 11 5. Overview . 12 6. Review of Data Models . 13 6.1. Information Exchange Models . 13 6.1.1. Flight Information Exchange Model (FIXM). 13 6.1.2. Aeronautical Information Exchange (AIXM) Model. 13 6.1.3. Weather Information Exchange Model (WXXM) . 14 6.1.4. NASA Air Traffic Management (ATM) Model . 14 6.2. Service description models . 19 6.2.1. Service Description Conceptual Model (SDCM) . 19 6.2.2. Web Service Description Ontological Model (WSDOM). 23 6.2.3. SWIM Documentation Controlled Vocabulary (FAA) . 25 7. Semantic Enablement Approaches . 27 8. Metadata level semantic enablement . 33 8.1. Issues with existing metadata standards . 34 8.1.1. Identification of Resources. 34 8.1.2. Resolvable URI. 34 8.1.3. Multilingual Support . 35 8.1.4. External Resource Descriptions . 35 8.1.5. Controlled Vocabulary Management . 36 8.1.6. Keywords Types . 37 8.1.7. Keyword Labeling Inconsistencies . -
Contents the Three Languages Theory In
Ie Contents The Three LanguagesTheory in Information Retrieval Part-controlled Vocabulary for Literature Studies UDC: International Medium Edition - English Text Class Number Searching in an Experimental Online Catalog UDC 168 + International Classification Vol. 13 (1986) Nr. 3 025.4 + 001.4 (05) INTERNATIONAL CLASSIFICATION Devoted to Concept Theory, Systematic Ter minology and Organization of Knowledge Editors Dr. phil. Ingetraut Dahlberg, 0-6000 Frank furt 50, Woogstr. 36a, Editor-in-chief Prof. Dr. med. Dr. phil. Alwin Diemer, Philo sophisches Institut der Universitat Dusseldorf, D-4000 Dusseldorf 1, Universitatsstr. 1, FRG. Prof. Jean M. Perreault, University Library, University of Alabama, P. O. B. 2600 Hunts Contents ville, Alabama 35807, USA Prof. Arashanipalai Neelameghan, clo Unes Editorial co PGI. 7, Place de Fontenoy, F-75700 Paris New Uses for Old Schemes 125 co-sponsored by - FID/CR (Federation Internationale de Do Articles cumentation, Committee on Classification Re G.Deschatelets: The three languages theory in information retrieval. 126 search, address see Dr. I. Dahlberg K.Harris: Part-controlled vocabulary for literature studies ..... 133 A.Chatterjee, G.G.Choudhury: UDC: International Medium Edition - Consulting Editors Mrs. Jean Aitchison, 12, Sollershott West, English text ....... ,. .. ....... ... .... 137 K.Markey: Class number searching in an experimental online catalog 142 Letchworth, Herts., SG6 3PX, England Prof. Asterio T. Campos, Departamento de Bi Reports and Communications . ... .. 151 blioteconomia, Universidade de Brasilia, Bra CSNA Annual Meeting 1986 - COMPSTAT 1986 - Fall Meeting of SEK DA-NK, silia OF, Brazil Gesellschaft flir Klassifikation - Stability in Classification - Dr. A.1. Cernyj, VINITI, Moscow A-219 Bal Standardization in Computerized Lexicography - Going for Gold - tijskaja u1. -
What Are Controlled Vocabularies?
2. What Are Controlled Vocabularies? A controlled vocabulary is an organized arrangement of words and phrases used to index content and/or to retrieve content through browsing or searching. It typically includes preferred and variant terms and has a defined scope or describes a specific domain. 2.1. Purpose of Controlled Vocabularies The purpose of controlled vocabularies is to organize information and to provide terminology to catalog and retrieve information. While capturing the richness of variant terms, controlled vocabularies also promote consistency in preferred terms and the assignment of the same terms to similar content. Given that a shared goal of the cultural heritage community is to improve access to visual arts and material culture information, controlled vocabularies are essential. They are necessary at the indexing phase because without them catalogers will not consistently use the same term to refer to the same person, place, or thing. In the retrieval process, various end users may use different synonyms or more generic terms to refer to a given concept. End users are often not specialists and thus need to be guided because they may not know the correct term. The most important functions of a controlled vocabulary are to gather together variant terms and synonyms for concepts and to link concepts in a logical order or sort them into categories. Are a rose window and a Catherine wheel the same thing? How is pot-metal glass related to the more general term stained glass? The links and relationships in a controlled vocabulary ensure that these connections are defined and maintained, for both cataloging and retrieval. -
Taxonomy Directed Folksonomies
2nd Version Date : 19/06/2007 TAXONOMY DIRECTED FOLKSONOMIES Integrating user tagging and controlled vocabularies for Australian education networks Sarah Hayman and Nick Lothian education.au Adelaide Australia Meeting: 157 Classification and Indexing Simultaneous Interpretation: No WORLD LIBRARY AND INFORMATION CONGRESS: 73RD IFLA GENERAL CONFERENCE AND COUNCIL 19-23 August 2007, Durban, South Africa http://www.ifla.org/iv/ifla73/index.htm 1 Abstract What is the role of controlled vocabulary in a Web 2.0 world? Can we have the best of both worlds: balancing folksonomies and controlled vocabularies to help communities of users find and share information and resources most relevant to them? education.au develops and manages Australian online services for education and training. Its goal is to bring people, learning and technology together. education.au projects are increasingly involved in exploring the use of Web 2.0 developments building on user ideas, knowledge and experience, and how these might be integrated with existing information management systems. This paper presents work being undertaken in this area, particularly in relation to controlled vocabularies, and discusses the challenges faced. Education Network Australia (edna) is a leading online resource collection and collaborative network for education, with an extensive repository of selected educational resources with metadata created by educators and information specialists. It uses controlled vocabularies for metadata creation and searching, where users receive suggested related terms from an education thesaurus, with their results. We recognise that no formal thesaurus can keep pace with user needs so are interested in exploiting the power of folksonomies. We describe a proof of concept project to develop community contributions to managing information and resources, using Taxonomy-Directed Folksonomy. -
Download the 2021 IEEE Thesaurus
2021 IEEE Thesaurus Version 1.0 Created by The Institute of Electrical and Electronics Engineers (IEEE) 2021 IEEE Thesaurus The IEEE Thesaurus is a controlled The IEEE Thesaurus also provides a vocabulary of almost 10,900 descriptive conceptual map through the use of engineering, technical and scientific terms, semantic relationships such as broader as well as IEEE-specific society terms terms (BT), narrower terms (NT), 'used for' [referred to as “descriptors” or “preferred relationships (USE/UF), and related terms terms”] .* Each descriptor included in the (RT). These semantic relationships identify thesaurus represents a single concept or theoretical connections between terms. unit of thought. The descriptors are Italic text denotes Non-preferred terms. considered the preferred terms for use in Bold text is used for preferred headings. describing IEEE content. The scope of descriptors is based on the material presented in IEEE journals, conference Abbreviations used in the Thesaurus: papers, standards, and/or IEEE organizational material. A controlled BT - Broader term vocabulary is a specific terminology used in NT - Narrower term a consistent and controlled fashion that RT - Related term results in better information searching and USE- Use preferred term retrieval. UF - Used for Thesaurus construction is based on the ANSI/NISO Z39.19-2005(2010) standard, Guidelines for the Construction, Format, and Management of Monolingual Controlled Vocabulary. The Thesaurus vocabulary uses American-based spellings with cross references to British variant spellings. The scope and structure of the IEEE Thesaurus reflects the engineering and scientific disciplines that comprise the Societies, Councils, and Communities of the IEEE in *Refer to ANSI/NISO NISO Z39.19-2005 addition to the technologies IEEE serves. -
Controlled Vocabulary and Folksonomies
Controlled Vocabulary and Folksonomies Louise Spiteri School of Information Management What are folksonomies? Folksonomies (known also as “social classifications”) are user created metadata. They are grassroots community classification of digital assets. The term “folksonomy” was created by Thomas Vander Val and represents a merging of the terms “folk” and “taxonomy.” One form of explicit user created metadata was popularized in the late 1990s with link-focused websites called weblogs. Where are folksonomies found? Folksonomies are found in social bookmarks managers such as Del.icio.us (http://del.icio.us/) and Furl (http://www.furl.net/), which allow users to: Add bookmarks of sites they like to their personal collections of links Organize and categorize these sites by adding their own terms, or tags Share this collection with other people with the same interests. The tags are used to collocate bookmarks: (a) within a user’s collection; and (b) across the entire system, e.g., the page http://del.icio.us/tag/blogging will show all bookmarks that are tagged with “blogging” by any user. Inter-term relationships There are no clearly defined relations between and among the terms in the vocabulary, unlike formal taxonomies and classification schemes, where there are multiple kinds of explicit relationships (e.g., broader, narrower, and related terms) between and among terms. Folksonomies are simply the set of terms that a group of users tagged content with; they are not a predetermined set of classification terms or labels. Popular folksonomy sites Del.icio.us (http://del.icio.us) Flickr (http://www.flickr.com) Frassle (http://www.frassle.org) Furl (http://www.furl.net) Simpy (http://www.simpy.com) Spurl (http://www.spurl.com) Technorati (http://www.technorati.com) How folksonomies work Registration is free. -
Controlled Vocabularies: an Overview
Controlled Vocabularies: an Overview MURTHA BACA DESCRIPTIVE METADATA WORKSHOP AT REED COLLEGE MAY 28, 2010 TYPOLOGY of DATA STANDARDS Data structure standards (metadata element sets): MARC, EAD, Dublin Core, CDWA, VRA Core Categories Data content standards (cataloging rules): AACR (ÎRDA), ISBD, CCO, DACS Data value standards (vocabularies): LCSH, LCNAF, TGM, AAT, ULAN , TGN, MeSH DtData forma tstddtandards (tdd(standards expressed in machine-readable form): MARC, MARCXML, MODS, EAD, CDWA Lite XML, Dublin Core Simple XML schema, DC Qualified XML schema, VRA Core XML schema M. Baca: Overview of Controlled Vocabularies Workshop at Reed College, 2010-05-28 1 What are vocabularies? y Maps to guide people to information { creating / filli ng { searching / researching { organizing / classifying / thinking y Collections of terminology where relationships between terms are represented y Data value standards (i.e. what is used to “fill” metadata elements/categories or “containers” of information) What are vocabularies? “Knowledge bases” -- bodies of knowledge represented by language (glossaries, dictionaries, thesauri, word lists) M. Baca: Overview of Controlled Vocabularies Workshop at Reed College, 2010-05-28 2 Types of terms in vocabularies personal names: Collate, Charles B. geographic names: Campbeltown (Argyll and Bute, SldScotland, UK) object names: clack valve corporate names: Cambrian Railways iconographic subjects and themes: The Legend of John Henry genre terms: political cartoons, fish stories multilingual equivalents: flat -
Controlled Vocabularies in the Digital Age
CONTROLLED VOCABULARIES IN THE DIGITAL AGE: ARE THEY STILL RELEVANT? William Andrew Baker Dissertation Prepared for the Degree of DOCTOR OF PHILOSOPHY UNIVERSITY OF NORTH TEXAS August 2017 APPROVED: Guillermo Oyarce, Committee Chair Brian O’Connor, Committee Member Will Senn, Committee Member Suliman Hawamdeh, Chair of the Department of Information Science Kinshuk, Dean of the College of Information Victor Prybutok, Dean of the Toulouse Graduate School Baker, William Andrew. Controlled Vocabularies in the Digital Age: Are They Still Relevant? Doctor of Philosophy (Information Science), August 2017, 147 pp., 22 tables, 7 figures, references, 105 titles. Keyword searching and controlled vocabularies such as Library of Congress subject headings (LCSH) proved to work well together in automated technologies and the two systems have been considered complimentary. When the Internet burst onto the information landscape, users embraced the simplicity of keyword searching of this resource while researchers and scholars seemed unable to agree on how best to make use of controlled vocabularies in this huge database. This research looked at a controlled vocabulary, LCSH, in the context of keyword searching of a full text database. The Internet and probably its most used search engine, Google, seemed to have set a standard that users have embraced: a keyword-searchable single search box on an uncluttered web page. Libraries have even introduced federated single search boxes to their web pages, another testimony to the influence of Google. UNT's Thesis and Dissertation digital database was used to compile quantitative data with the results input into an EXCEL spreadsheet. Both Library of Congress subject headings (LCSH) and author-assigned keywords were analyzed within selected dissertations and both systems were compared. -
A Controlled Vocabulary and Metadata Schema for Materials Science Data
A Controlled Vocabulary and Metadata Schema for Materials Science Data Discovery PRACTICE PAPER ANDREA MEDINA-SMITH CHANDLER A. BECKER RAYMOND L. PLANTE LAURA M. BARTOLO ALDEN DIMA JAMES A. WARREN ROBERT J. HANISCH *Author affiliations can be found in the back matter of this article ABSTRACT CORRESPONDING AUTHOR: Andrea Medina-Smith The International Materials Resource Registries (IMRR) working group of the Research National Institute of Standards Data Alliance (RDA) was created to spur initial development of a federated registry and Technology, Information system to allow for easier discovery and access to materials data. As part of this effort, Services Office, Gaithersburg, a controlled vocabulary and metadata schema were developed with contributions MD, United States of America from members of the working group and other experts. Here we describe the process, [email protected] the resulting vocabulary and XML schema, and lessons learned in the development and use of the schema. KEYWORDS: controlled vocabulary; metadata schema; materials science; RDA; federated registry system TO CITE THIS ARTICLE: Medina-Smith, A, Becker, CA, Plante, RL, Bartolo, LM, Dima, A, Warren, JA and Hanisch, RJ. 2021. A Controlled Vocabulary and Metadata Schema for Materials Science Data Discovery. Data Science Journal, 20: 18, pp. 1–10. DOI: https://doi.org/10.5334/dsj- 2021-018 INTRODUCTION Medina-Smith et al. 2 Data Science Journal In early 2016, the Research Data Alliance (RDA) Working Group for International Materials DOI: 10.5334/dsj-2021- 018 Resource Registries (IMRR) was established to bring together experts in materials science and information technology to address the problem material science researchers face of finding and accessing data related to their work. -
Automatic Indexing: an Approach Using an Index Term Corpus and Combining Linguistic and Statistical Methods
CORE Metadata, citation and similar papers at core.ac.uk Provided by Helsingin yliopiston digitaalinen arkisto Automatic indexing: an approach using an index term corpus and combining linguistic and statistical methods Timo Lahtinen Academic dissertation to be publicly discussed, by due permission of the Faculty of Arts at the University of Helsinki in lecture room Unioninkatu 35, on the 11th of December, 2000, at 11 o’clock. University of Helsinki PUBLICATIONS Department of General Linguistics NO. 34 P.O. Box 4 2000 FIN-00014 University of Helsinki Finland ISBN 951-45-9639-0 ISBN 951-45-9640-4 (PDF) ISSN 0355-7170 Helsinki 2000 Yliopistopaino Abstract This thesis discusses the problems and the methods of finding relevant information in large collections of documents. The contribution of this thesis to this problem is to develop better content analysis methods which can be used to describe document content with index terms. Index terms can be used as meta-information that describes documents, and that is used for seeking information. The main point of this thesis is to illustrate the process of developing an automatic indexer which analyses the content of documents by combining evidence from word frequencies and evidence from linguistic analysis provided by a syntactic parser. The indexer weights the expressions of a text according to their estimated importance for describing the content of a given document on the basis of the content analysis. The typical linguistic features of index terms were explored using a linguistically analysed text collection where the index terms are manually marked up. This text collection is referred to as an index term corpus. -
NEDLIB Glossary
NEDLIB Glossary Authors: José Luis Borbinha, National Library of Portugal Fernando Cardoso, National Library of Portugal Nuno Freire, INESC Date of Issue: 22 February 2000 Issue: 1.0web Total Number of Pages: 46 NEDLIB Glossary TABLE OF CONTENTS TABLE OF CONTENTS...............................................................................................................................................................................i Abstract ................................................................................................................................................................................................2 Keywords..............................................................................................................................................................................................2 1. Concepts ..................................................................................................................................................................................................2 1.1 Index of Terms ............................................................................................................................................................................2 1.2 Terms Relationship ....................................................................................................................................................................6 1.3 Glossary ....................................................................................................................................................................................13