Recuperação de informação de música e dados ID3: possíveis aplicações

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliações externas e em larga escala nas redes de educação básica dos Estados brasileiros

Pós-graduação em Educação - FCT

A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an integrated and fine-grained comparison framework to deal with both structural and semantic similarities in XML documents (detecting the occurrences and repetitions of structurally and semantically similar sub-trees), and to allow the end-user to adjust the comparison process according to her requirements. Our framework consists of four main modules for (i) discovering the structural commonalities between sub-trees, (ii) identifying sub-tree semantic resemblances, (iii) computing tree-based edit operations costs, and (iv) computing tree edit distance. Experimental results demonstrate higher comparison accuracy with respect to alternative methods, while timing experiments reflect the impact of semantic similarity on overall system performance.

Unveiling the relationship between complex networks metrics and word senses

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012

A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems

Abstract Background Recent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for information processing systems in research centers. Additionally, the routines of genomics laboratory are typically characterized by high parallelism in testing and constant procedure changes. Results This paper describes a formal approach to address this challenge through the implementation of a genetic testing management system applied to human genome laboratory. We introduced the Human Genome Research Center Information System (CEGH) in Brazil, a system that is able to support constant changes in human genome testing and can provide patients updated results based on the most recent and validated genetic knowledge. Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP). The main difference between our approach and related works is that we were able to join two important aspects: 1) process scalability achieved through relational database implementation, and 2) correctness of processes using process algebra. Furthermore, the software allows end users to define genetic testing without requiring any knowledge about business process notation or process algebra. Conclusions This paper presents the CEGH information system that is a Laboratory Information Management System (LIMS) based on a formal framework to support genetic testing management for Mendelian disorder studies. We have proved the feasibility and showed usability benefits of a rigorous approach that is able to specify, validate, and perform genetic testing using easy end user interfaces.

FOLKSONOMIA: UMA ANÁLISE DE SUA OPERACIONALIDADE E SUA POSSÍVEL APLICABILIDADE NA CIÊNCIA DA INFORMAÇÃO

O artigo apresenta uma análise da operacionalidade das Folksonomias e a possibilidade de aplicação dessa ferramenta nos sistemas de organização da informação da área de Ciência da Informação. Para tanto foi realizada uma análise de coerência de tags e dos recursos disponíveis para etiquetagem em dois websites, a Last.fm e o CiteULike. Por meio dessa análise constatou-se que em ambos os websites ocorreram incoerências e discrepâncias nas tags utilizadas. Todavia, o sistema da Last.fm demonstrou-se mais funcional que o do CiteULike obtendo um desempenho melhor. Por fim, sugere-se a junção das Folksonomias às Ontologias, que permitiriam a criação de sistemas automatizados de organização de conteúdos informacionais alimentados pelos próprios usuários

Temporal PageRank

The our reality is characterized by a constant progress and, to follow that, people need to stay up to date on the events. In a world with a lot of existing news, search for the ideal ones may be difficult, because the obstacles that make it arduous will be expanded more and more over time, due to the enrichment of data. In response, a great help is given by Information Retrieval, an interdisciplinary branch of computer science that deals with the management and the retrieval of the information. An IR system is developed to search for contents, contained in a reference dataset, considered relevant with respect to the need expressed by an interrogative query. To satisfy these ambitions, we must consider that most of the developed IR systems rely solely on textual similarity to identify relevant information, defining them as such when they include one or more keywords expressed by the query. The idea studied here is that this is not always sufficient, especially when it's necessary to manage large databases, as is the web. The existing solutions may generate low quality responses not allowing, to the users, a valid navigation through them. The intuition, to overcome these limitations, has been to define a new concept of relevance, to differently rank the results. So, the light was given to Temporal PageRank, a new proposal for the Web Information Retrieval that relies on a combination of several factors to increase the quality of research on the web. Temporal PageRank incorporates the advantages of a ranking algorithm, to prefer the information reported by web pages considered important by the context itself in which they reside, and the potential of techniques belonging to the world of the Temporal Information Retrieval, exploiting the temporal aspects of data, describing their chronological contexts. In this thesis, the new proposal is discussed, comparing its results with those achieved by the best known solutions, analyzing its strengths and its weaknesses.

Specificity, duplex degradation and subcellular localization of antagomirs

MicroRNAs (miRNAs) are an abundant class of 20-23-nt long regulators of gene expression. The study of miRNA function in mice and potential therapeutic approaches largely depend on modified oligonucleotides. We recently demonstrated silencing miRNA function in mice using chemically modified and cholesterol-conjugated RNAs termed 'antagomirs'. Here, we further characterize the properties and function of antagomirs in mice. We demonstrate that antagomirs harbor optimized phosphorothioate modifications, require >19-nt length for highest efficiency and can discriminate between single nucleotide mismatches of the targeted miRNA. Degradation of different chemically protected miRNA/antagomir duplexes in mouse livers and localization of antagomirs in a cytosolic compartment that is distinct from processing (P)-bodies indicates a degradation mechanism independent of the RNA interference (RNAi) pathway. Finally, we show that antagomirs, although incapable of silencing miRNAs in the central nervous system (CNS) when injected systemically, efficiently target miRNAs when injected locally into the mouse cortex. Our data further validate the effectiveness of antagomirs in vivo and should facilitate future studies to silence miRNAs for functional analysis and in clinically relevant settings.

Matching Software and Context in Open Learning Scenarios

We describe the use of log file analysis to investigate whether the use of CSCL applications corresponds to its didactical purposes. Exemplarily we examine the use of the web-based system CommSy as software support for project-oriented university courses. We present two findings: (1) We suggest measures to shape the context of CSCL applications and support their initial and continuous use. (2) We show how log files can be used to analyze how, when and by whom a CSCL system is used and thus help to validate further empirical findings. However, log file analyses can only be interpreted reasonably when additional data concerning the context of use is available.

A distributed, semiotic-inductive and human-Oriented approach to web-scale knowledge retrieval

Web-scale knowledge retrieval can be enabled by distributed information retrieval, clustering Web clients to a large-scale computing infrastructure for knowledge discovery from Web documents. Based on this infrastructure, we propose to apply semiotic (i.e., sub-syntactical) and inductive (i.e., probabilistic) methods for inferring concept associations in human knowledge. These associations can be combined to form a fuzzy (i.e.,gradual) semantic net representing a map of the knowledge in the Web. Thus, we propose to provide interactive visualizations of these cognitive concept maps to end users, who can browse and search the Web in a human-oriented, visual, and associative interface.

Extraktion und kartographische Visualisierung von Informationen aus Weblogs

Beim Information Retrieval ist in Anbetracht der Informationsflut entscheidend, relevante Informationen zu finden. Ein vielversprechender Ansatz liegt im Semantischen Web, wobei dem System die Bedeutung von Informationen ontologiebasiert beigebracht wird. Sucht der Benutzer nach Stichworten, werden ihm anhand der Ontologie verwandte Begriffe angezeigt und er kann mittels Mensch-Maschine-Interaktion seine relevanten Informationen extrahieren. Um eine solche Interaktion zu fördern, werden die Ergebnisse visuell aufgearbeitet. Dabei liegt der Mehrwert darin, dass der Benutzer anstelle von Tausenden von Suchresultaten in einer fast endlosen Liste, ein kartographisch visualisiertes Suchresultat geliefert bekommt. Dabei hilft die Visualisierung, unvorhergesehene Beziehungen zu entdecken und zu erforschen.

Using citation data to improve retrieval from MEDLINE.

OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.

MIRACLE’s Ad-Hoc and Geographical IR Approaches for CLEF 2006

This paper presents the 2006 Miracle team’s approaches to the Ad-Hoc and Geographical Information Retrieval tasks. A first set of runs was obtained using a set of basic components. Then, by putting together special combinations of these runs, an extended set was obtained. With respect to previous campaigns some improvements have been introduced in our system: an entity recognition prototype is integrated in our tokenization scheme, and the performance of our indexing and retrieval engine has been improved. For GeoCLEF, we tested retrieving using geo-entity and textual references separately, and then combining them with different approaches.

Image Retrieval: The MIRACLE Approach

ImageCLEF is a pilot experiment run at CLEF 2003 for cross language image retrieval using textual captions related to image contents. In this paper, we describe the participation of the MIRACLE research team (Multilingual Information RetrievAl at CLEF), detailing the different experiments and discussing their preliminary results.