Analytical Study of Typographical Errors in OPACS and Corrective Measures

The present study is an attempt to highlight the problem of typographical errors in OPACS. The errors made while typing catalogue entries as well as importing bibliographical records from other libraries exist unnoticed by librarians resulting the non-retrieval of available records and affecting the quality of OPACs. This paper follows previous research on the topic mainly by Jeffrey Beall and Terry Ballard. The word “management” was chosen from the list of likely to be misspelled words identified by previous research. It was found that the word is wrongly entered in several forms in local, national and international OPACs justifying the observations of Ballard that typos occur in almost everywhere. Though there are lots of corrective measures proposed and are in use, the study asserts the fact that human effort is needed to get rid of the problem. The paper is also an invitation to the library professionals and system designers to construct a strategy to solve the issue

Hierarchies of conceptual scales

Formal Concept Analysis allows to derive conceptual hierarchies from data tables. Formal Concept Analysis is applied in various domains, e.g., data analysis, information retrieval, and knowledge discovery in databases. In order to deal with increasing sizes of the data tables (and to allow more complex data structures than just binary attributes), conceputal scales habe been developed. They are considered as metadata which structure the data conceptually. But in large applications, the number of conceptual scales increases as well. Techniques are needed which support the navigation of the user also on this meta-level of conceptual scales. In this paper, we attack this problem by extending the set of scales by hierarchically ordered higher level scales and by introducing a visualization technique called nested scaling. We extend the two-level architecture of Formal Concept Analysis (the data table plus one level of conceptual scales) to many-level architecture with a cascading system of conceptual scales. The approach also allows to use representation techniques of Formal Concept Analysis for the visualization of thesauri and ontologies.

Statistical Models for Co-occurrence Data

Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from two finite sets. The main challenge for statistical models in this context is to overcome the inherent data sparseness and to estimate the probabilities for pairs which were rarely observed or even unobserved in a given sample set. Moreover, it is often of considerable interest to extract grouping structure or to find a hierarchical data organization. A novel family of mixture models is proposed which explain the observed data by a finite number of shared aspects or clusters. This provides a common framework for statistical inference and structure discovery and also includes several recently proposed models as special cases. Adopting the maximum likelihood principle, EM algorithms are derived to fit the model parameters. We develop improved versions of EM which largely avoid overfitting problems and overcome the inherent locality of EM--based optimization. Among the broad variety of possible applications, e.g., in information retrieval, natural language processing, data mining, and computer vision, we have chosen document retrieval, the statistical analysis of noun/adjective co-occurrence and the unsupervised segmentation of textured images to test and evaluate the proposed algorithms.

LBD LOCAL: un sistema para la recuperación de documentos con referencias geográficas

Tanto los Sistemas de Información Geográfica como la Recuperación de Información han sido campos de investigación muy importantes en las últimas décadas. Recientemente, un nuevo campo de investigación llamado Recuperación de Información Geográfica ha surgido fruto de la confluencia de estos dos campos. El objetivo principal de este campo es definir estructuras de indexación y técnicas para almacenar y recuperar documentos de manera eficiente empleando tanto las referencias textuales como las referencias geográficas contenidas en el texto. En este artículo presentamos la arquitectura de un sistema para recuperación de información geográfica y definimos el flujo de trabajo para la extracción de las referencias geográficas de los documentos. Presentamos además una nueva estructura de indexación que combina un índice invertido, un índice espacial y una ontología. Esta estructura mejora las capacidades de consulta de otras propuestas

Estrategias de búsqueda y utilización de la información en trabajos académicos en educación superior

Los nuevos Planes de Estudio adaptados al Espacio Europeo de la Educación Superior ya están implantándose en nuestras universidades y esto ha supuesto plantearnos qué cambios debemos incorporar como docentes, para que nuestros estudiantes consigan las competencias, habilidades y destrezas necesarias para ser profesionales competentes en un futuro cercano

Full Text Library Resources for INFO1010

These slides support students in understanding how to respond to the challenge of: "I’ve been told not to use Google or Wikipedia to research my essay. What else is there?" The powerpoint guides students in how to identify high quality, up to date and relevant resources on the web that they can reliably draw upon for their academic assignments. The slides were created by the subject liaison librarian who supports the School of Electronics and Computer Science at the University of Southampton, Fiona Nichols.

Physics Information Skills

An introduction to high quality information resources and databases in Physics and related disciplines. Refers to resources of information and methods of searching them.

Understanding climate data through commentary metadata: the CHARMe project

We describe the CHARMe project, which aims to link climate datasets with publications, user feedback and other items of "commentary metadata". The system will help users learn from previous community experience and select datasets that best suit their needs, as well as providing direct traceability between conclusions and the data that supported them. The project applies the principles of Linked Data and adopts the Open Annotation standard to record and publish commentary information. CHARMe contributes to the emerging landscape of "climate services", which will provide climate data and information to influence policy and decision-making. Although the project focuses on climate science, the technologies and concepts are very general and could be applied to other fields.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 Elsevier Inc. All rights reserved.

Privacy Advocacy in Libraries in the Age of Mass Surveillance

Alison Macrina is the founder and director of the Library Freedom Project, an initiative that aims to make real the promise of intellectual freedom in libraries. The Library Freedom Project trains librarians on the state of global surveillance, privacy rights, and privacy-protecting technology, so that librarians may in turn teach their communities about safeguarding privacy. In 2015, Alison was named one of Library Journal‘s Movers and Shakers. Read more about the Library Freedom Project at libraryfreedomproject.org.

A recuperação da informação sob a ótica dos usuários: um estudo de caso da base de dados Accessus

Trata das questões de organização e recuperação da informação no caso específico do acervo do Centro de Pesquisa e História Contemporânea do Brasil – CPDOC. Baseia essa análise num estudo de caso do uso do serviço de referência da instituição prestado pela Sala de Consulta e também no utilização da base de dados Accessus. Traça um perfil do usuário do acervo da instituição além de um perfil de pesquisa desses indivíduos ao mapear o comportamento dos usuários diante da ferramenta Accessus. Aborda o contexto da elaboração da base de dados e investiga a criação da linguagem controlada em história e ciências afins que serviu de base para o Accessus. Problematiza as questões de acessibilidade da linguagem a um público não relacionado com a área. Pareia essa problematização com análise dos diferentes perfis de usuários. Discute a forma de indexação do acervo do CPDOC e suscita reflexões sobre esse processo que considere uma relação direta com o perfil dos usuários.

Ampliando o acesso livre a informação: a digitalização do acervo do Núcleo Temático da Seca

FREIRE, Isa Maria, et al. Ampliando o acesso livre a informaçao: a digitalizaçao do acervo do Nucleo Tematico da Seca. Pesquisa Brasileira em Ciência da Informação e Biblioteconomia, v.3, n.2, p. 137-142, 2008.Disponivel em: . Acesso em: 04 nov. 2010.