Thesis (Ph. D.)--Drexel University, 1994. / eContent provider-neutral record in process. Description based on print version record.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1972. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliography.

Orientador: Walter Moreira / Banca: Vânia Mara Alves de Lima / Banca: Mariângela Spotti Lopes Fujita / Resumo: A diversidade na produção de informações oriundas de instituições públicas e privadas proporciona desafios diários para a arquivística, os quais se referem a maneira pela qual a área irá possibilitar o acesso, a recuperação e a reutilização das informações. Neste contexto dinâmico, fazer uso de instrumentos que colaborem com o processo de gestão da informação torna-se primordial. Além dos instrumentos tradicionalmente utilizados pela arquivística, tais como a descrição e a classificação arquivística, faz-se necessário a adoção de instrumentos que possam complementá-los, agregando precisão ao conjunto de descritores utilizados nos processos de representação da informação. Nesse sentido, esta pesquisa, estabelece discussões que visam a aproximar os instrumentos tradicionais da arquivística dos vocabulários controlados. Entende-se que esses instrumentos atuam como mediadores entre a representação e a recuperação da informação. Objetivou-se, portanto, analisar a norma de descrição arquivística ISAAR (CPF) que trata da descrição de registro de autoridade arquivística para entidades coletivas, pessoas e famílias, em conjunto com a norma ISO 25964-2011 que trata da elaboração de vocabulários controlados. Dessa forma, propôs-se verificar a interface entre os vocabulários controlados e a norma de descrição ISAAR (CPF), considerando-se que suas confluências podem melhorar o processo de representação e recuperação da informação. Para tanto, utilizaram-se os seguintes procedimentos metod... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The diversity in the production of information from public and private institutions provide daily challenges for archiving, which refer to how the area will provide access, recovery and reuse of information. In this dynamic context, make use of instruments to collaborate with the information management process becomes paramount. In addition to the instruments traditionally used for archiving, such as the description and the archival classification, it is necessary to adopt tools that can complement them, adding precision to the set of descriptors used in the representation of information processes. In this sense, this research establishes discussions aimed at bringing the traditional instruments of archival of controlled vocabularies. It is understood that these instruments act as mediators between the representation and retrieval of information. In this sense, this research establishes discussions aimed at bringing the traditional instruments of archival of controlled vocabularies. It is understood that these instruments act as mediators between the representation and retrieval of information. The objective is therefore to this research, collaborate with the studies on the development of specific vocabularies for files because it was observed that there is still a shortfall in relation to the theme proposed under archival. In addition, it also aimed to analyze the standard of archival description ISAAR (CPF) which deals with the archival authority record description for corp... (Complete abstract click electronic access below) / Mestre

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No. of bitstreams: 1 volpato_esn_dr_bot.pdf: 2811609 bytes, checksum: 80bb3a313f1b7220a03a2d560f6d0719 (MD5) Previous issue date: 2017-07-24 / Introdução: Para auxiliar os pesquisadores a identificarem os termos que devem compor a estratégia de busca, bibliotecários e educadores orientam os pesquisadores a consultarem e incluírem os termos (autorizados e não autorizados) do vocabulário controlado da base de dados na formulação de estratégias sensíveis para elaboração de revisões sistemáticas. No entanto, ao utilizar todos os termos disponíveis no tesauros (i.e. vocabulário controlado), as estratégias podem ficar extensas, pois alguns descritores incluem muitos termos não autorizados. Objetivo: Avaliar a praticidade e abrangência das estratégias de buscas compostas por descritores tanto do MeSH como do EMTREE, na área de Anestesiologia, que possam compor uma única estratégia de busca a ser utilizada nas bases de dados MEDLINE via PubMed e EMBASE. Método: Em nosso estudo transversal de estratégias de busca, selecionamos e analisamos 37 estratégias de busca desenvolvidas para o campo de Anestesiologia. Foram elaboradas as estratégias de busca originais que incluíram todos os termos disponibilizados nos vocabulários controlados, ou seja, com todas as variações referentes às diferentes grafias e ordens, direta e indireta, analisadas neste estudo. As estratégias originais foram modificadas com a exclusão dos termos que eram uma variação de grafia ou da ordem (direta ou indireta) para comparação dos resultados e adaptadas para submissão nas duas bases de dados. Resultados: As estratégias originais (com inclusão das variações: diferentes grafias e ordens direta e indireta) recuperaram o mesmo número de registros que as estratégias modificadas (sem a inclusão das variações)na base de dados Medline (média de 61,3%) e maior número na EMBASE (média de 63,9 %), na amostra analisada. O número de resultados obtidos pelas pesquisas analisadas não foi idêntico usando a associação ou não dos termos MeSH e EMTREE, sendo que a associação dos termos dos dois vocabulários controlados recuperou maior número de registros em comparação com o uso de termos de apenas um deles, nas duas bases de dados estudadas. Conclusões: Considerando os resultados, recomendamos o uso de todos os termos disponíveis nos vocabulários controlados incluindo termos autorizados e não autorizados (ou seja, diferentes ortografias e ordem direta e indireta do mesmo termo) e a associação dos termos do MeSH com os do EMTREE, para elaboração de estratégias de busca altamente sensíveis na realização de revisões sistemáticas. / Introduction: A high-quality electronic search is essential in ensuring accuracy and comprehensivness in identifying potentially relevant records in conducting a systematic review. To assist researchers in identifying terms when formulating a sensitive search strategy, librarians and educators instruct researchers to consult and include preferred and non-preferred terms of the controlled database. However, by using all available terms in the thesaurus (i.e. subject headings), strategies can be lengthy and very laborious. Objective: To identify the most efficient method for searching in both Medline through PubMed and EMBASE, covering search terms with different spellings, direct and indirect orders, and association (or lack thereof) with MeSH and EMTREE terms. Method: In our cross-sectional study of search strategies, we selected and analysed 37 search strategies specifically developed for the anesthesiology field. These search strategies were adapted in order to cover all potentially relevant search terms in terms of different spellings and direct and indirect orders, most efficiently. Results: When adapted to include different spellings and direct and indirect orders, adapted versions of the selected search strategies retrieved the same number of search results in the Medline (mean of 61,3%) and higher number in EMBASE (mean of 63,9%) of the analyzed sample. The number of results retrieved by the searches analysed was not identical using the association or not of MeSH and EMTREE terms; however the association of these terms from both controlled vocabularies retireved a large number of records compared to the use of either one of them. Conclusions: In view of these results, we recommend the use of search terms which include preferred and non-preferred terms (i.e., different spellings and direct/indirect order of the same term) and associated MeSH and EMTREE terms, in order to develop highly-sensitive search strategies for systematic reviews.

This study is concerned with determining how library patrons decide which entries to select from a subject card file which consists of numerous bibliographic records. Patrons are expected to select certain records based on elements that are objective (displayed directly by the record and requiring little or no interpretation) or subjective (recognized because of the user's special knowledge of the field).

With the ever increase in biomedical literature, text-mining has emerged as an important technology to support bio-curation and search. Word sense disambiguation (WSD), the correct identification of terms in text in the light of ambiguity, is an important problem in text-mining. Since the late 1940s many approaches based on supervised (decision trees, naive Bayes, neural networks, support vector machines) and unsupervised machine learning (context-clustering, word-clustering, co-occurrence graphs) have been developed. Knowledge-based methods that make use of the WordNet computational lexicon have also been developed. But only few make use of ontologies, i.e. hierarchical controlled vocabularies, to solve the problem and none exploit inference over ontologies and the use of metadata from publications. This thesis addresses the WSD problem in biomedical ontologies by suggesting different approaches for word sense disambiguation that use ontologies and metadata. The "Closest Sense" method assumes that the ontology defines multiple senses of the term; it computes the shortest path of co-occurring terms in the document to one of these senses. The "Term Cooc" method defines a log-odds ratio for co-occurring terms including inferred co-occurrences. The "MetaData" approach trains a classifier on metadata; it does not require any ontology, but requires training data, which the other methods do not. These approaches are compared to each other when applied to a manually curated training corpus of 2600 documents for seven ambiguous terms from the Gene Ontology and MeSH. All approaches over all conditions achieve 80% success rate on average. The MetaData approach performs best with 96%, when trained on high-quality data. Its performance deteriorates as quality of the training data decreases. The Term Cooc approach performs better on Gene Ontology (92% success) than on MeSH (73% success) as MeSH is not a strict is-a/part-of, but rather a loose is-related-to hierarchy. The Closest Sense approach achieves on average 80% success rate. Furthermore, the thesis showcases applications ranging from ontology design to semantic search where WSD is important.

The patent domain is a very important source of scientific information that is currently not used to its full potential. Searching for relevant patents is a complex task because the number of existing patents is very high and grows quickly, patent text is extremely complicated, and standard vocabulary is not used consistently or doesn’t even exist. As a consequence, pure keyword searches often fail to return satisfying results in the patent domain. Major companies employ patent professionals who are able to search patents effectively, but even they have to invest a lot of time and effort into their search. Academic scientists on the other hand do not have access to such resources and therefore often do not search patents at all, but they risk missing up-to-date information that will not be published in scientific publications until much later, if it is published at all. Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Similarly, professional patent searches expand beyond keywords by including class codes from various patent classification systems. However, classification-based searches can only be performed effectively if the user has very detailed knowledge of the system, which is usually not the case for academic scientists. Consequently, we investigated methods to automatically identify relevant classes that can then be suggested to the user to expand their query. Since every patent is assigned at least one class code, it should be possible for these assignments to be used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. In order to gain such knowledge, we perform an in-depth comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows that the hierarchies are structurally similar, but terms and annotations differ significantly. The most important differences concern the considerably higher complexity of the IPC class definitions compared to MeSH terms and the far lower number of class assignments to the average patent compared to the number of MeSH terms assigned to PubMed documents. As a result of these differences, problems are caused both for unexperienced patent searchers and professionals. On the one hand, the complex term system makes it very difficult for members of the former group to find any IPC classes that are relevant for their search task. On the other hand, the low number of IPC classes per patent points to incomplete class assignments by the patent office, therefore limiting the recall of the classification-based searches that are frequently performed by the latter group. We approach these problems from two directions: First, by automatically assigning additional patent classes to make up for the missing assignments, and second, by automatically retrieving relevant keywords and classes that are proposed to the user so they can expand their initial search. For the automated assignment of additional patent classes, we adapt an approach to the patent domain that was successfully used for the assignment of MeSH terms to PubMed abstracts. Each document is assigned a set of IPC classes by a large set of binary Maximum-Entropy classifiers. Our evaluation shows good performance by individual classifiers (precision/recall between 0:84 and 0:90), making the retrieval of additional relevant documents for specific IPC classes feasible. The assignment of additional classes to specific documents is more problematic, since the precision of our classifiers is not high enough to avoid false positives. However, we propose filtering methods that can help solve this problem. For the guided patent search, we demonstrate various methods to expand a user’s initial query. Our methods use both keywords and class codes that the user enters to retrieve additional relevant keywords and classes that are then suggested to the user. These additional query components are extracted from different sources such as patent text, IPC definitions, external vocabularies and co-occurrence data. The suggested expansions can help unexperienced users refine their queries with relevant IPC classes, and professionals can compose their complete query faster and more easily. We also present GoPatents, a patent retrieval prototype that incorporates some of our proposals and makes faceted browsing of a patent corpus possible.

The patent domain is a very important source of scientific information that is currently not used to its full potential. Searching for relevant patents is a complex task because the number of existing patents is very high and grows quickly, patent text is extremely complicated, and standard vocabulary is not used consistently or doesn’t even exist. As a consequence, pure keyword searches often fail to return satisfying results in the patent domain. Major companies employ patent professionals who are able to search patents effectively, but even they have to invest a lot of time and effort into their search. Academic scientists on the other hand do not have access to such resources and therefore often do not search patents at all, but they risk missing up-to-date information that will not be published in scientific publications until much later, if it is published at all. Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Similarly, professional patent searches expand beyond keywords by including class codes from various patent classification systems. However, classification-based searches can only be performed effectively if the user has very detailed knowledge of the system, which is usually not the case for academic scientists. Consequently, we investigated methods to automatically identify relevant classes that can then be suggested to the user to expand their query. Since every patent is assigned at least one class code, it should be possible for these assignments to be used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. In order to gain such knowledge, we perform an in-depth comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows that the hierarchies are structurally similar, but terms and annotations differ significantly. The most important differences concern the considerably higher complexity of the IPC class definitions compared to MeSH terms and the far lower number of class assignments to the average patent compared to the number of MeSH terms assigned to PubMed documents. As a result of these differences, problems are caused both for unexperienced patent searchers and professionals. On the one hand, the complex term system makes it very difficult for members of the former group to find any IPC classes that are relevant for their search task. On the other hand, the low number of IPC classes per patent points to incomplete class assignments by the patent office, therefore limiting the recall of the classification-based searches that are frequently performed by the latter group. We approach these problems from two directions: First, by automatically assigning additional patent classes to make up for the missing assignments, and second, by automatically retrieving relevant keywords and classes that are proposed to the user so they can expand their initial search. For the automated assignment of additional patent classes, we adapt an approach to the patent domain that was successfully used for the assignment of MeSH terms to PubMed abstracts. Each document is assigned a set of IPC classes by a large set of binary Maximum-Entropy classifiers. Our evaluation shows good performance by individual classifiers (precision/recall between 0:84 and 0:90), making the retrieval of additional relevant documents for specific IPC classes feasible. The assignment of additional classes to specific documents is more problematic, since the precision of our classifiers is not high enough to avoid false positives. However, we propose filtering methods that can help solve this problem. For the guided patent search, we demonstrate various methods to expand a user’s initial query. Our methods use both keywords and class codes that the user enters to retrieve additional relevant keywords and classes that are then suggested to the user. These additional query components are extracted from different sources such as patent text, IPC definitions, external vocabularies and co-occurrence data. The suggested expansions can help unexperienced users refine their queries with relevant IPC classes, and professionals can compose their complete query faster and more easily. We also present GoPatents, a patent retrieval prototype that incorporates some of our proposals and makes faceted browsing of a patent corpus possible.

With the ever increase in biomedical literature, text-mining has emerged as an important technology to support bio-curation and search. Word sense disambiguation (WSD), the correct identification of terms in text in the light of ambiguity, is an important problem in text-mining. Since the late 1940s many approaches based on supervised (decision trees, naive Bayes, neural networks, support vector machines) and unsupervised machine learning (context-clustering, word-clustering, co-occurrence graphs) have been developed. Knowledge-based methods that make use of the WordNet computational lexicon have also been developed. But only few make use of ontologies, i.e. hierarchical controlled vocabularies, to solve the problem and none exploit inference over ontologies and the use of metadata from publications. This thesis addresses the WSD problem in biomedical ontologies by suggesting different approaches for word sense disambiguation that use ontologies and metadata. The "Closest Sense" method assumes that the ontology defines multiple senses of the term; it computes the shortest path of co-occurring terms in the document to one of these senses. The "Term Cooc" method defines a log-odds ratio for co-occurring terms including inferred co-occurrences. The "MetaData" approach trains a classifier on metadata; it does not require any ontology, but requires training data, which the other methods do not. These approaches are compared to each other when applied to a manually curated training corpus of 2600 documents for seven ambiguous terms from the Gene Ontology and MeSH. All approaches over all conditions achieve 80% success rate on average. The MetaData approach performs best with 96%, when trained on high-quality data. Its performance deteriorates as quality of the training data decreases. The Term Cooc approach performs better on Gene Ontology (92% success) than on MeSH (73% success) as MeSH is not a strict is-a/part-of, but rather a loose is-related-to hierarchy. The Closest Sense approach achieves on average 80% success rate. Furthermore, the thesis showcases applications ranging from ontology design to semantic search where WSD is important.

The purpose of this essay is to investigate the construction of sexuality in Swedish school libraries through the lens of Foucault’s concept of power/knowledge. Five libraries were investigated, using two kinds of method: discourse analysis of titles, classification codes and subject headings of sex education materials, and qualitative interviews with librarians working at each of the libraries. Most sex education materials were classified within the medicine section, illustrating the continued authority of medical discourse in matters concerning sexuality. Books for younger audiences were often written for either boys or girls, reproducing a binary understanding of gender. Most library collections had a larger number of books written for girls, indicating that girls’ sexualities are subjected to a higher degree of discipline compared to boys’. Collections illustrated two combating discourses about female sexuality, one focusing on problematic aspects of sexuality such as rape and sexual abuse, the other constructing female sexuality as connected to lust and pleasure. Sex and love were constructed as intrinsically linked though the frequent use of the word “love” in titles and the concurrence of the subject heading “love” with “sex” or “sexuality”. Indexation patterns made LGBTQ-people stand out as exceptions to the heterosexual norm. Informants had differing views on what they thought should be part of their libraries’ sex education materials. Some mentioned a range of different subjects; others emphasized the need for “basic” books. When asked what materials they would not purchase informants mentioned pornographic, racist, violent and otherwise “offensive” materials. Most informants did not actively promote the libraries’ collections of sex education materials apart from signposting certain books. Regardless of the level of cooperation between teachers and librarians none of the informants had ever participated in the schools’ sex education classes. This is a two years master’s thesis in Archive, Library and Museum Studies.