Linguistic patterns have been used widely in shallow methods to develop numerous NLP applications. Approaches for acquiring linguistic patterns can be broadly categorised into three groups: supervised learning, unsupervised learning and manual methods. In supervised learning approaches, a large annotated training corpus is required for the learning algorithms to achieve decent results. However, annotated corpora are expensive to obtain and usually available only for established tasks. Unsupervised learning approaches usually start with a few seed examples and gather some statistics based on a large unannotated corpus to detect new examples that are similar to the seed ones. Most of these approaches either populate lexicons for predefined patterns or learn new patterns for extracting general factual information; hence they are applicable to only a limited number of tasks. Manually creating linguistic patterns has the advantage of utilising an expert's knowledge to overcome the scarcity of annotated data. In tasks with no annotated data available, the manual way seems to be the only choice. One typical problem that occurs with manual approaches is that the combination of multiple patterns, possibly being used at different stages of processing, often causes unintended side effects. Existing approaches, however, do not focus on the practical problem of acquiring those patterns but rather on how to use linguistic patterns for processing text. A systematic way to support the process of manually acquiring linguistic patterns in an efficient manner is long overdue. This thesis presents KAFTIE, an incremental knowledge acquisition framework that strongly supports experts in creating linguistic patterns manually for various NLP tasks. KAFTIE addresses difficulties in manually constructing knowledge bases of linguistic patterns, or rules in general, often faced in existing approaches by: (1) offering a systematic way to create new patterns while ensuring they are consistent; (2) alleviating the difficulty in choosing the right level of generality when creating a new pattern; (3) suggesting how existing patterns can be modified to improve the knowledge base's performance; (4) making the effort in creating a new pattern, or modifying an existing pattern, independent of the knowledge base's size. KAFTIE, therefore, makes it possible for experts to efficiently build large knowledge bases for complex tasks. This thesis also presents the KAFDIS framework for discourse processing using new representation formalisms: the level-of-detail tree and the discourse structure graph.

Skyline has been proposed as an important operator for many applications, such as multi-criteria decision making, data mining and visualization, and user-preference queries. Due to its importance, skyline and its computation have received considerable attention from database research community recently. All the existing techniques, however, focus on the conventional databases. They are not applicable to online computation environment, such as data stream. In addition, the existing studies consider efficiency of skyline computation only, while the fundamental problem on the semantics of skylines still remains open. In this thesis, we study three problems of skyline computation: (1) online computing skyline over data stream; (2) skyline cube computation and its analysis; and (3) top-k most representative skyline. To tackle the problem of online skyline computation, we develop a novel framework which converts more expensive multiple dimensional skyline computation to stabbing queries in 1-dimensional space. Based on this framework, a rigorous theoretical analysis of the time complexity of online skyline computation is provided. Then, efficient algorithms are proposed to support ad hoc and continuous skyline queries over data stream. Inspired by the idea of data cube, we propose a novel concept of skyline cube which consists of skylines of all possible non-empty subsets of a given full space. We identify the unique sharing strategies for skyline cube computation and develop two efficient algorithms which compute skyline cube in a bottom-up and top-down manner, respectively. Finally, a theoretical framework to answer the question about semantics of skyline and analysis of multidimensional subspace skyline are presented. Motived by the fact that the full skyline may be less informative because it generally consists of a large number of skyline points, we proposed a novel skyline operator -- top-k most representative skyline. The top-k most representative skyline operator selects the k skyline points so that the number of data points, which are dominated by at least one of these k skyline points, is maximized. To compute top-k most representative skyline, two efficient algorithms and their theoretical analysis are presented.

Skyline has been proposed as an important operator for many applications, such as multi-criteria decision making, data mining and visualization, and user-preference queries. Due to its importance, skyline and its computation have received considerable attention from database research community recently. All the existing techniques, however, focus on the conventional databases. They are not applicable to online computation environment, such as data stream. In addition, the existing studies consider efficiency of skyline computation only, while the fundamental problem on the semantics of skylines still remains open. In this thesis, we study three problems of skyline computation: (1) online computing skyline over data stream; (2) skyline cube computation and its analysis; and (3) top-k most representative skyline. To tackle the problem of online skyline computation, we develop a novel framework which converts more expensive multiple dimensional skyline computation to stabbing queries in 1-dimensional space. Based on this framework, a rigorous theoretical analysis of the time complexity of online skyline computation is provided. Then, efficient algorithms are proposed to support ad hoc and continuous skyline queries over data stream. Inspired by the idea of data cube, we propose a novel concept of skyline cube which consists of skylines of all possible non-empty subsets of a given full space. We identify the unique sharing strategies for skyline cube computation and develop two efficient algorithms which compute skyline cube in a bottom-up and top-down manner, respectively. Finally, a theoretical framework to answer the question about semantics of skyline and analysis of multidimensional subspace skyline are presented. Motived by the fact that the full skyline may be less informative because it generally consists of a large number of skyline points, we proposed a novel skyline operator -- top-k most representative skyline. The top-k most representative skyline operator selects the k skyline points so that the number of data points, which are dominated by at least one of these k skyline points, is maximized. To compute top-k most representative skyline, two efficient algorithms and their theoretical analysis are presented.

Skyline has been proposed as an important operator for many applications, such as multi-criteria decision making, data mining and visualization, and user-preference queries. Due to its importance, skyline and its computation have received considerable attention from database research community recently. All the existing techniques, however, focus on the conventional databases. They are not applicable to online computation environment, such as data stream. In addition, the existing studies consider efficiency of skyline computation only, while the fundamental problem on the semantics of skylines still remains open. In this thesis, we study three problems of skyline computation: (1) online computing skyline over data stream; (2) skyline cube computation and its analysis; and (3) top-k most representative skyline. To tackle the problem of online skyline computation, we develop a novel framework which converts more expensive multiple dimensional skyline computation to stabbing queries in 1-dimensional space. Based on this framework, a rigorous theoretical analysis of the time complexity of online skyline computation is provided. Then, efficient algorithms are proposed to support ad hoc and continuous skyline queries over data stream. Inspired by the idea of data cube, we propose a novel concept of skyline cube which consists of skylines of all possible non-empty subsets of a given full space. We identify the unique sharing strategies for skyline cube computation and develop two efficient algorithms which compute skyline cube in a bottom-up and top-down manner, respectively. Finally, a theoretical framework to answer the question about semantics of skyline and analysis of multidimensional subspace skyline are presented. Motived by the fact that the full skyline may be less informative because it generally consists of a large number of skyline points, we proposed a novel skyline operator -- top-k most representative skyline. The top-k most representative skyline operator selects the k skyline points so that the number of data points, which are dominated by at least one of these k skyline points, is maximized. To compute top-k most representative skyline, two efficient algorithms and their theoretical analysis are presented.

In all natural languages, ambiguity is a universal phenomenon. When a word has multiple meaning depending on its contexts it is called an ambiguous word. The process of determining the correct meaning of a word (formally named word sense) in a given context is word sense disambiguation(WSD). WSD is one of the most fundamental problems in natural language processing. If properly addressed, it could lead to revolutionary advancement in many other technologies such as text search engine technology, automatic text summarization and classification, automatic lexicon construction, machine translation and automatic learning agent technology. One difficulty that has always confronted WSD researchers is the lack of high quality sense specific information. For example, if the word "power" Immediately preceds the word "plant", it would strongly constrain the meaning of "plant" to be "an industrial facility". If "power" is replaced by the phrase "root of a", then the sense of "plant" is dictated to be "an organism" of the kingdom Planate. It is obvious that manually building a comprehensive sense specific information base for each sense of each word is impractical. Researchers also tried to extract such information from large dictionaries as well as manually sense tagged corpora. Most of the dictionaries used for WSD are not built for this purpose and have a lot of inherited peculiarities. While manual tagging is slow and costly, automatic tagging is not successful in providing a reliable performance. Furthermore, it is often the case that for a randomly chosen word (to be disambiguated), the sense specific context corpora that can be collected from dictionaries are not large enough. Therefore, manually building sense specific information bases or extraction of such information from dictionaries are not effective approaches to obtain sense specific information. A web text, due to its vast quantity and wide diversity, becomes an ideal source for extraction of large quantity of sense specific information. In this thesis, the impacts of Web texts on various aspects of WSD has been investigated. New measures and models are proposed to tame enormous amount of Web texts for the purpose of WSD. They are formally evaluated by experimenting their disambiguation performance on about 70 ambiguous nouns. The results are very encouraging and have helped revealing the great potential of using Web texts for WSD. The results are published in three papers at Australia national and international level (Wang&Hoffmann,2004,2005,2006)[42][43][44].

Skyline has been proposed as an important operator for many applications, such as multi-criteria decision making, data mining and visualization, and user-preference queries. Due to its importance, skyline and its computation have received considerable attention from database research community recently. All the existing techniques, however, focus on the conventional databases. They are not applicable to online computation environment, such as data stream. In addition, the existing studies consider efficiency of skyline computation only, while the fundamental problem on the semantics of skylines still remains open. In this thesis, we study three problems of skyline computation: (1) online computing skyline over data stream; (2) skyline cube computation and its analysis; and (3) top-k most representative skyline. To tackle the problem of online skyline computation, we develop a novel framework which converts more expensive multiple dimensional skyline computation to stabbing queries in 1-dimensional space. Based on this framework, a rigorous theoretical analysis of the time complexity of online skyline computation is provided. Then, efficient algorithms are proposed to support ad hoc and continuous skyline queries over data stream. Inspired by the idea of data cube, we propose a novel concept of skyline cube which consists of skylines of all possible non-empty subsets of a given full space. We identify the unique sharing strategies for skyline cube computation and develop two efficient algorithms which compute skyline cube in a bottom-up and top-down manner, respectively. Finally, a theoretical framework to answer the question about semantics of skyline and analysis of multidimensional subspace skyline are presented. Motived by the fact that the full skyline may be less informative because it generally consists of a large number of skyline points, we proposed a novel skyline operator -- top-k most representative skyline. The top-k most representative skyline operator selects the k skyline points so that the number of data points, which are dominated by at least one of these k skyline points, is maximized. To compute top-k most representative skyline, two efficient algorithms and their theoretical analysis are presented.

Orientador: Claudia Maria Bauzer Medeiros / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-18T01:37:29Z (GMT). No. of bitstreams: 1 VitalianoFilho_ArnaldoFrancisco_M.pdf: 2435279 bytes, checksum: d273e44a51be70002d918835c2e5c11b (MD5) Previous issue date: 2009 / Resumo: O compartilhamento de informações, processos e modelos de experimentos entre cientistas de diferentes organizações e domínios do conhecimento vem aumentando com a disponibilização dessas informações e modelos na Web. Muitos destes modelos de experimentos são descritos como workflows científicos. Entretanto, não existe uma padronização para a sua descrição, dificultando assim o reaproveitamento de workflows e seus componentes já existentes. A dissertação contribui para a solução deste problema com os seguintes resultados: a análise dos problemas relativos ao compartilhamento e projeto cooperativo de workflows científicos na Web, análise de aspectos de semântica e metadados relacionados a estes workflows, a disponibilização de um editor Web de workflows usando padrões WFMC e, o desenvolvimento de um modelo de anotação semântica para workflows científicos. Com isto, a dissertação cria a base para permitir a descoberta, reuso e compartilhamento de workflows científicos nas Web. O editor permite que pesquisadores construam seus workflows e anotações de forma online, e permite o consequente teste, com dados externos, do sistema de anotações / Abstract: The sharing of information, processes and models of experiments is increasing among scientists from many organizations and areas of knowledge, and thus there is a need for supply mechanisms of workflow discovery. Many of these models are described as scientific workflows. However, there is no default specification to describe them, which complicates the reuse of workflows and components that are available. This thesis contributes to solving this problem by presenting the following results: analysis of issues related to the sharing and cooperative design of scientific workflows on the Web; analysis of semantic aspects and metadata related to workflows, the development of a Web-based workflow editor, which incorporates our semantic annotation model for scientific workflows. Given these factors, this work creates the basis to allow the discovery, reuse and sharing of scientific workflows in the Web / Mestrado / Banco de Dados / Mestre em Ciência da Computação

Orientador: Claudia Maria Bauzer Medeiros / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-15T17:25:33Z (GMT). No. of bitstreams: 1 Sousa_SidneyRobertode_M.pdf: 6639415 bytes, checksum: fbd426bff26dda1788b1310f9167190c (MD5) Previous issue date: 2010 / Resumo: Sistemas de informação geográfica a cada vez mais utilizam informação geo-espacial da Web para produzir informação geográfica. Um grande desafio para tais sistemas é encontrar dados relevantes, onde tal busca é frequentemente baseada em palavras-chave ou nome de arquivos. Porém, tais abordagens carecem de semântica. Desta forma, torna-se necessário oferecer mecanismos para preparação de dados, afim de auxiliar a recuperação de dados semanticamente relevantes. Para atacar este problema, esta dissertação de mestrado propôem uma arquitetura baseada em serviços para gerenciar anotações semânticas. Neste trabalho, uma anotação semântica é um conjunto de triplas - chamadas unidades de anotação semântica - <subject, metadata field, object >, onde subject é um documento geo-espacial, (metadata field) é um campo de metadados sobre este documento e object é um termo de ontologia que associa semanticamente o campo de metadados a algum conceito apropriado. As principais contribuições desta dissertação são: um estudo comparativo sobre ferramentas de anotação; especificação e implementação de uma arquitetura baseada em serviços para gerenciar anotações semânticas, incluindo serviços para manuseio de termos de ontologias; e uma análise comparativa de mecanismos para armazenar anotações semânticas. O trabalho toma como estudo de caso anotações semânticas sobre documentos agrícolas / Abstract: Geographic information systems (GIS) are increasingly using geospatial data from the Web to produce geographic information. One big challenge is to find the relevant data, which often is based on keywords or even file names. However, these approaches lack semantics. Thus, it is necessary to provide mechanisms to prepare data to help retrieval of semantically relevant data. To attack this problem, this dissertation proposes a service-based architecture to manage semantic annotations. In this work, a semantic annotation is a set of triples - called semantic annotation units - < subject? metadata field? object >, where subject is a geospatial resource, (metadata field) contains some characteristic about this resource, and object is an ontology term that semantically associates the metadata field to some appropriate concept. The main contributions of this dissertation are: a comparative study on annotation tools; specification and implementation of a service-based architecture to manage semantic annotations, including services for handling ontology terms; and a comparative analysis of mechanisms for storing semantic annotations. The work takes as case study semantic annotations about agricultural resources / Mestrado / Banco de Dados / Mestre em Ciência da Computação

This thesis introduces an innovative methodology of combining some traditional dictionary based approaches to word sense disambiguation (semantic similarity measures and overlap of word glosses, both based on WordNet) with some graph-based centrality methods, namely the degree of the vertices, Pagerank, closeness, and betweenness. The approach is completely unsupervised, and is based on creating graphs for the words to be disambiguated. We experiment with several possible combinations of the semantic similarity measures as the first stage in our experiments. The next stage attempts to score individual vertices in the graphs previously created based on several graph connectivity measures. During the final stage, several voting schemes are applied on the results obtained from the different centrality algorithms. The most important contributions of this work are not only that it is a novel approach and it works well, but also that it has great potential in overcoming the new-knowledge-acquisition bottleneck which has apparently brought research in supervised WSD as an explicit application to a plateau. The type of research reported in this thesis, which does not require manually annotated data, holds promise of a lot of new and interesting things, and our work is one of the first steps, despite being a small one, in this direction. The complete system is built and tested on standard benchmarks, and is comparable with work done on graph-based word sense disambiguation as well as lexical chains. The evaluation indicates that the right combination of the above mentioned metrics can be used to develop an unsupervised disambiguation engine as powerful as the state-of-the-art in WSD.

Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institution or a river shore. Finding the correct meaning of a word in a particular context is a task known as word sense disambiguation (WSD), which is essential for many natural language processing applications such as machine translation, information retrieval, and others. While most current WSD methods try to disambiguate a small number of words for which enough annotated examples are available, the method proposed in this thesis attempts to address all words in unrestricted text. The method is based on constraints imposed by syntactic dependencies and concept generalizations drawn from an external dictionary. The method was tested on standard benchmarks as used during the SENSEVAL-2 and SENSEVAL-3 WSD international evaluation exercises, and was found to be competitive.