Thesis (Ph. D.)--George Mason University, 2008. / Vita: p. 177. Thesis director: Larry Kerschberg. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed July 3, 2008). Includes bibliographical references (p. 169-176). Also issued in print.

In multimedia databases, data are images, audio, video, texts, etc. Research interests in these types of databases have increased in the last decade or so, especially with the advent of the Internet and Semantic Web. Fundamental research issues vary from unified data modelling, retrieval of data items and dynamic nature of updates. The thesis builds on findings in Semantic Web and retrieval techniques and explores novel tagging methods for identifying data items. Tagging systems have become popular which enable the users to add tags to Internet resources such as images, video and audio to make them more manageable. Collaborative tagging is concerned with the relationship between people and resources. Most of these resources have metadata in machine processable format and enable users to use free- text keywords (so-called tags) as search techniques. This research references some tagging systems, e.g. Flicker, delicious and myweb2.0. The limitation with such techniques includes polysemy (one word and different meaning), synonymy (different words and one meaning), different lexical forms (singular, plural, and conjugated words) and misspelling errors or alternate spellings. The work presented in this thesis introduces semantic characterization of web resources that describes the structure and organization of tagging, aiming to extend the existing Multimedia Query using similarity measures to cater for collaborative tagging. In addition, we discuss the semantic difficulties of tagging systems, suggesting improvements in their accuracies. The scope of our work is classified as follows: (i) Increase the accuracy and confidence of multimedia tagging systems. (ii) Increase the similarity measures of images by integrating varieties of measures. To address the first shortcoming, we use the WordNet based on a tagging system for social sharing and retrieval of images as a semantic lingual ontology resource. For the second shortcoming we use the similarity measures in different ways to recognise the multimedia tagging system. Fundamental to our work is the novel information model that we have constructed for our computation. This is based on the fact that an image is a rich object that can be characterised and formulated in n-dimensions, each dimension contains valuable information that will help in increasing the accuracy of the search. For example an image of a tree in a forest contains more information than an image of the same tree but in a different environment. In this thesis we characterise a data item (an image) by a primary description, followed by n-secondary descriptions. As n increases, the accuracy of the search improves. We give various techniques to analyse data and its associated query. To increase the accuracy of the tagging system we have performed different experiments on many images using similarity measures and various techniques from VoI (Value of Information). The findings have shown the linkage/integration between similarity measures and that VoI improves searches and helps/guides a tagger in choosing the most adequate of tags.

Zugl.: Karlsruhe, University, Diss., 2007.

Thesis (doctoral)--Humboldt Universität, Berlin, 2003. / Description based on print version record. Includes bibliographical references (p. [107]-113) and index. Also issued online.

<div>When a user types in a search query in an Information Retrieval system, a list of top ‘n’ ranked documents relevant to the query are returned by the system. Relevant means not just returning documents that belong to the same category as that of the search query, but also returning documents that provide a concise answer to the search query. Determining the relevance of the documents is a significant challenge as the classic indexing techniques that use term/word frequencies do not consider the term (word) dependencies or the impact of previous terms on the current words or the meaning of the words in the document. There is a need to model the dependencies of the terms in the text data and learn the underlying statistical patterns to find the similarity between the user query and the documents to determine the relevancy.</div><div><br></div><div>This research proposes a solution based on Markov Random Fields (MRF) and Restricted Boltzmann Machines (RBM) to solve the problem of term dependencies and learn the underlying patterns to return documents that are very similar to the user query.</div>

No description available.

Made available in DSpace on 2016-04-04T18:36:40Z (GMT). No. of bitstreams: 1 Rodrigo Aquino de Carvalho.pdf: 1679875 bytes, checksum: fa32bdb2c592532c696f6356ca40494e (MD5) Previous issue date: 2009-02-27 / The Web became the main vehicle of the information storage and dissemination, but the information in this environment must be organized by efficient manner. The research objective was identify perspectives in the Semantic Web to the Information Science, through the literature mapping of basic proposals of this technology and of its development and through the analysis of the theses and dissertations about the subject presented to the Graduate Programs of Information Science in Brazil. Dialectical method with exploratory and descriptive approach and bibliographical technical procedures allied with bibliometrics and content analysis were applied. The following movements and relations were studied: - the origin of the Web to the actual status of the Semantic Web (qualitative change); - the actual Web and the Semantic Web (movement, qualitative change); - Information Science (through the literature review) and Semantic Web (relations); and Information Science (in theses and dissertations of the Brazil) and Semantic Web (relations). The movement of the current Web to the Semantic Web was significant, with objectives dedicated to make possible complex inferences about machines. Leaving the simplicity of the architecture of the current Web, the Semantic Web grew little, it didn't have significant qualitative movement. With relationship to Information Science, your architecture sustains a relationship. The field of Information Science, in what concerns to the literature of retrieval and representation of the information and representation of the knowledge presents resources that can collaborate for the development of the Semantic Web (as thesaurus, metadata etc.). Other point that favours that approach is the interdisciplinary relationship of the field with the Computational Science, with the Artificial Intelligence and with the Linguistics (Terminology). But it exists the need that the knowledge on the generated knowledge Information Science on the Semantic Web surpasses the limits of the proper field. / A Web se tornou o principal ve?culo de armazenamento e dissemina??o da informa??o, mas h? a necessidade de que a informa??o nesse ambiente seja tratada de forma eficiente. O objetivo da pesquisa ? buscar perspectivas na Web Sem?ntica para a Ci?ncia da Informa??o, atrav?s do mapeamento na literatura das propostas b?sicas dessa tecnologia e do desenvolvimento j? alcan?ado e tamb?m da an?lise das teses e disserta??es sobre o tema apresentado aos Programas de P?sgradua??o em Ci?ncia da Informa??o no Brasil. O m?todo utilizado foi o dial?tico, com abordagem explorat?ria e descritiva, o procedimento t?cnico foi o bibliogr?fico e as t?cnicas para a estrutura??o dos dados foram a bibliometria e a an?lise de conte?do. Foram estudados os seguintes movimentos e rela??es: - do surgimento da Web at? o estado atual da Web Sem?ntica (mudan?a qualitativa); - Web atual e Web Sem?ntica (movimento, mudan?a qualitativa); - Ci?ncia da Informa??o (atrav?s da revis?o de literatura) e Web sem?ntica (rela??es); e - Ci?ncia da Informa??o (em teses e disserta??es do Brasil) e Web Sem?ntica (rela??es). O movimento da Web atual ? Web Sem?ntica foi significativo, com objetivos voltados a possibilitar infer?ncias complexas por parte das m?quinas, mas deixando a simplicidade da arquitetura da Web atual, a Web Sem?ntica se desenvolveu pouco, n?o teve movimento qualitativo significativo. Quanto ? CI, sua arquitetura sustenta uma rela??o. A literatura da CI, no que diz respeito ? recupera??o e representa??o da informa??o e representa??o do conhecimento, possui recursos que podem colaborar para o desenvolvimento da Web Sem?ntica (como os tesauros, os metadados etc,). Outro ponto que favorece essa aproxima??o ? a rela??o interdisciplinar da ?rea com a Ci?ncia da Computa??o, com a Intelig?ncia Artificial e com a Ling??stica (Terminologia). Mas existe a necessidade de que o conhecimento gerado na CI, sobre a Web Sem?ntica ultrapasse os limites da pr?pria ?rea.

Thesis (doctoral) - Humboldt Universität, Berlin, 2003. / Includes bibliographical references (p. [107]-113) and index. Also issued online.

No description available.

Das Thema Information Retrieval hat insbesondere in Form von Internetsuchmaschinen eine große Bedeutung erlangt. Retrievalsysteme werden für eine Vielzahl unterschiedlicher Rechercheszenarien eingesetzt, unter anderem für firmeninterne Supportdatenbanken, aber auch für die Organisation persönlicher E-Mails. Eine aktuelle Herausforderung besteht in der Bestimmung und Vorhersage der Leistungsfähigkeit einzelner Komponenten dieser Retrievalsysteme, insbesondere der komplexen Wechselwirkungen zwischen ihnen. Für die Implementierung und Konfiguration der Retrievalsysteme und der Retrievalkomponenten werden Fachleute benötigt. Mithilfe der webbasierten Lernanwendung Xtrieval Web Lab können Studierende praktisches Wissen über den Information Retrieval Prozess erwerben, indem sie Retrievalkomponenten zu einem Retrievalsystem zusammenstellen und evaluieren, ohne dafür eine Programmiersprache einsetzen zu müssen. Spielemechaniken leiten die Studierenden bei ihrem Entdeckungsprozess an, motivieren sie und verhindern eine Informationsüberladung durch eine Aufteilung der Lerninhalte. / Information retrieval has achieved great significance in form of search engines for the Internet. Retrieval systems are used in a variety of research scenarios, including corporate support databases, but also for the organization of personal emails. A current challenge is to determine and predict the performance of individual components of these retrieval systems, in particular the complex interactions between them. For the implementation and configuration of retrieval systems and retrieval components professionals are needed. By using the web-based learning application Xtrieval Web Lab students can gain practical knowledge about the information retrieval process by arranging retrieval components in a retrieval system and their evaluation without using a programming language. Game mechanics guide the students in their discovery process, motivate them and prevent information overload by a partition of the learning content.