Tendências em Processos e Sistemas da Organização do Conhecimento de textos narrativos de ficção

We analyze the latest publications on fiction knowledge organization, retrieved from the ISKO database Knowledge Organization Literature, aiming to outline the current situation of the area as well as to identify trends. We used Hjørland's categories of Knowledge Organization Processes and Knowledge Organization Systems for the analysis. We extracted textual discursive units from each text to support their categorization. We conclude that since 2010 the trend is to work on knowledge organization processes of fiction as even those publications that dealt with fiction knowledge organization systems also discussed the processes.Se analizan los últimos trabajos publicados sobre organización del conocimiento de ficción, recuperados de la base de datos de ISKO Knowledge Organization Literature, con el objetivo de retratar la situación actual e identificar tendencias en el área. Se utilizan como categorías de análisis la distinción de Procesos de Organización del Conocimiento y Sistemas de Organización del Conocimiento de Hjørland. Para cada trabajo se extrajeron unidades textuales discursivas que apoyaron su categorización. Se concluye que a partir de 2010 hay una tendencia hacia los procesos de organización del conocimiento de ficción ya que incluso en los trabajos en que se trabajaron los sistemas de organización del conocimiento estos coexistieron con discusiones sobre los procesos

Efficient Parameter Estimation for Information Retrieval Using Black-Box Optimization

open4openCosta A.; Di Buccio E.; Melucci M.; Nannicini G.Costa, A.; Di Buccio, E.; Melucci, M.; Nannicini, G

Feature based dynamic intra-video indexing

A thesis submitted in partial fulfillment for the degree of Doctor of PhilosophyWith the advent of digital imagery and its wide spread application in all vistas of life, it has become an important component in the world of communication. Video content ranging from broadcast news, sports, personal videos, surveillance, movies and entertainment and similar domains is increasing exponentially in quantity and it is becoming a challenge to retrieve content of interest from the corpora. This has led to an increased interest amongst the researchers to investigate concepts of video structure analysis, feature extraction, content annotation, tagging, video indexing, querying and retrieval to fulfil the requirements. However, most of the previous work is confined within specific domain and constrained by the quality, processing and storage capabilities. This thesis presents a novel framework agglomerating the established approaches from feature extraction to browsing in one system of content based video retrieval. The proposed framework significantly fills the gap identified while satisfying the imposed constraints of processing, storage, quality and retrieval times. The output entails a framework, methodology and prototype application to allow the user to efficiently and effectively retrieved content of interest such as age, gender and activity by specifying the relevant query. Experiments have shown plausible results with an average precision and recall of 0.91 and 0.92 respectively for face detection using Haar wavelets based approach. Precision of age ranges from 0.82 to 0.91 and recall from 0.78 to 0.84. The recognition of gender gives better precision with males (0.89) compared to females while recall gives a higher value with females (0.92). Activity of the subject has been detected using Hough transform and classified using Hiddell Markov Model. A comprehensive dataset to support similar studies has also been developed as part of the research process. A Graphical User Interface (GUI) providing a friendly and intuitive interface has been integrated into the developed system to facilitate the retrieval process. The comparison results of the intraclass correlation coefficient (ICC) shows that the performance of the system closely resembles with that of the human annotator. The performance has been optimised for time and error rate

Index ordering by query-independent measures

There is an ever-increasing amount of data that is being produced from various data sources — this data must then be organised effectively if we hope to search though it. Traditional information retrieval approaches search through all available data in a particular collection in order to find the most suitable results, however, for particularly large collections this may be extremely time consuming. Our purposed solution to this problem is to only search a limited amount of the collection at query-time, in order to speed this retrieval process up. Although, in doing this we aim to limit the loss in retrieval efficacy (in terms of accuracy of results). The way we aim to do this is to firstly identify the most “important” documents within the collection, and then sort the documents within the collection in order of their "importance” in the collection. In this way we can choose to limit the amount of information to search through, by eliminating the documents of lesser importance, which should not only make the search more efficient, but should also limit any loss in retrieval accuracy. In this thesis we investigate various different query-independent methods that may indicate the importance of a document in a collection. The more accurate the measure is at determining an important document, the more effectively we can eliminate documents from the retrieval process - improving the query-throughput of the system, as well as providing a high level of accuracy in the returned results. The effectiveness of these approaches are evaluated using the datasets provided by the terabyte track at the Text REtreival Conference (TREC)