Uso de ontologias para detecção de padrões de análise em modelos conceituais em bibliotecas digitais de componentes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduaçõa em Ciência da Computação.Apresenta-se neste trabalho um método de detecção de padrões de analise (PA#s) em modelos conceituais utilizando ontologias. Um PA pode ter sido previsto ou não no momento em que o modelo conceitual foi concebido. Mesmo se a análise do sistema (fase onde surge o modelo conceitual) não for orientada pelos padrões de análise, é possível verificar a ocorrências destes dentro dos modelos produzidos. Esta ocorrência se dá a partir de algumas regras que são observadas e apresentadas neste trabalho. Para detectar PA em modelos conceituais o artefato essencial integrante deste método é uma ontologia. A ontologia como ferramenta para representar conhecimento tem como papel no CompogeMatch (método apresentado neste trabalho) identificar os conceitos existentes nos modelos submetidos ao método. Uma vez detectados os PAs existentes nos modelos, é possível criar índices a partir desses PA#s encontrados e utilizá-los como filtros indexados no processo de recuperação em bibliotecas digitais de componentes ou modelos conceituais de software. Uma alternativa às buscas por meio de palavras-chaves que apresentam algumas limitações, como por exemplo, não identificação de palavras sinônimas. Por fim, esta pesquisa indica como esse processo de busca pode trazer resultados superiores à busca por palavras-chaves quando o que está se procurando são modelos conceituais ou, mais precisamente, software

Bridging semantic gap: learning and integrating semantics for content-based retrieval

Digital cameras have entered ordinary homes and produced^incredibly large number of photos. As a typical example of broad image domain, unconstrained consumer photos vary significantly. Unlike professional or domain-specific images, the objects in the photos are ill-posed, occluded, and cluttered with poor lighting, focus, and exposure. Content-based image retrieval research has yet to bridge the semantic gap between computable low-level information and high-level user interpretation. In this thesis, we address the issue of semantic gap with a structured learning framework to allow modular extraction of visual semantics. Semantic image regions (e.g. face, building, sky etc) are learned statistically, detected directly from image without segmentation, reconciled across multiple scales, and aggregated spatially to form compact semantic index. To circumvent the ambiguity and subjectivity in a query, a new query method that allows spatial arrangement of visual semantics is proposed. A query is represented as a disjunctive normal form of visual query terms and processed using fuzzy set operators. A drawback of supervised learning is the manual labeling of regions as training samples. In this thesis, a new learning framework to discover local semantic patterns and to generate their samples for training with minimal human intervention has been developed. The discovered patterns can be visualized and used in semantic indexing. In addition, three new class-based indexing schemes are explored. The winnertake- all scheme supports class-based image retrieval. The class relative scheme and the local classification scheme compute inter-class memberships and local class patterns as indexes for similarity matching respectively. A Bayesian formulation is proposed to unify local and global indexes in image comparison and ranking that resulted in superior image retrieval performance over those of single indexes. Query-by-example experiments on 2400 consumer photos with 16 semantic queries show that the proposed approaches have significantly better (18% to 55%) average precisions than a high-dimension feature fusion approach. The thesis has paved two promising research directions, namely the semantics design approach and the semantics discovery approach. They form elegant dual frameworks that exploits pattern classifiers in learning and integrating local and global image semantics

Toward a Visual Thesaurus

A thesaurus is a book containing synonyms in a given language; it provides similarity links when trying to retrieve articles or stories about a particular topic. A "visual thesaurus" works with pictures, not words. It aids in recognizing visually similar events, "visual synonyms," including both spatial and motion similarity. This paper describes a method for building such a tool, and recent research results in the MIT Media Lab which contribute toward this goal. The heart of the method is a learning system which gathers information by interacting with a user of a database. The learning system is also capable of incorporating audio and other perceptual information, ultimately constructing a representation of common sense knowledge. 1 Introduction Collections of digital imagery are growing at a rapid pace. The contexts are broad, including areas such as entertainment (e.g. searching for a funny movie scene), education (e.g. hunting down illustrations for a book report), science (e.g. ..