3 research outputs found

    Knowledge discovery in multi-relational graphs

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    Ante el reducido abanico de metodologías para llevar a cabo tareas de aprendizaje automático relacional, el objetivo principal de esta tesis es realizar un análisis de los métodos existentes, modificando u optimizando en la medida de lo posible algunos de ellos, y aportar nuevos métodos que proporcionen nuevas vías para abordar esta difícil tarea. Para ello, y sin nombrar objetivos relacionados con revisiones bibliográficas ni comparativas entre modelos e implementaciones, se plantean una serie de objetivos concretos a ser cubiertos: 1. Definir estructuras flexibles y potentes que permitan modelar fenómenos en base a los elementos que los componen y a las relaciones establecidas entre éstos. Dichas estructuras deben poder expresar de manera natural propiedades complejas (valores continuos o categóricos, vectores, matrices, diccionarios, grafos,...) de los elementos, así como relaciones heterogéneas entre éstos que a su vez puedan poseer el mismo nivel de propiedades complejas. Además, dichas estructuras deben permitir modelar fenómenos en los que las relaciones entre los elementos no siempre se dan de forma binaria (intervienen únicamente dos elementos), sino que puedan intervenir un número cualquiera de ellos. 2. Definir herramientas para construir, manipular y medir dichas estructuras. Por muy potente y flexible que sea una estructura, será de poca utilidad si no se poseen las herramientas adecuadas para manipularla y estudiarla. Estas herramientas deben ser eficientes en su implementación y cubrir labores de construcción y consulta. 3. Desarrollar nuevos algoritmos de aprendizaje automático relacional de caja negra. En aquellas tareas en las que nuestro objetivo no es obtener modelos explicativos, podremos permitirnos utilizar modelos de caja negra, sacrificando la interpretabilidad a favor de una mayor eficiencia computacional. 4. Desarrollar nuevos algoritmos de aprendizaje automático relacional de caja blanca. Cuando estamos interesados en una explicación acerca del funcionamiento de los sistemas que se analizan, buscaremos modelos de aprendizaje automático de caja blanca. 5. Mejorar las herramientas de consulta, análisis y reparación para bases de datos. Algunas de las consultas a larga distancia en bases de datos presentan un coste computacional demasiado alto, lo que impide realizar análisis adecuados en algunos sistemas de información. Además, las bases de datos en grafo carecen de métodos que permitan normalizar o reparar los datos de manera automática o bajo la supervisión de un humano. Es interesante aproximarse al desarrollo de herramientas que lleven a cabo este tipo de tareas aumentando la eficiencia y ofreciendo una nueva capa de consulta y normalización que permita curar los datos para un almacenamiento y una recuperación más óptimos. Todos los objetivos marcados son desarrollados sobre una base formal sólida, basada en Teoría de la Información, Teoría del Aprendizaje, Teoría de Redes Neuronales Artificiales y Teoría de Grafos. Esta base permite que los resultados obtenidos sean suficientemente formales como para que los aportes que se realicen puedan ser fácilmente evaluados. Además, los modelos abstractos desarrollados son fácilmente implementables sobre máquinas reales para poder verificar experimentalmente su funcionamiento y poder ofrecer a la comunidad científica soluciones útiles en un corto espacio de tiempo

    Exploring the potential of complexity theory in urban regeneration processes.

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    The research project was conceived out of a desire to explore the potential application of complexity theory in understanding urban regeneration processes. The science is still in its infancy, having been born out of a general milieu of, and paralleled, the dissatisfaction with the classical science approach to the problems of the world. At the heart of the complexity project is, therefore, the defiance of the reductionist paradigm in favour of holism and emphasis on emergent properties in the understanding of complex systems. As a new way of thinking and one that boasts of its ability to cut across disciplinary boundaries, the emerging science has found its maiden expression in many spheres of the social and physical inquiry - offering, in each case, potential solutions to the vexing problems and questions that have survived the test of time. In urban studies, such questions reside within the general thesis of the persistence of the urban problem in the midst of a myriad of theoretical tools and policies designed to secure a better understanding and tackle the problem. The translation of this promising theoretical platform into the study and the pursuit of the research agenda were conducted through the case study of the Hulme inner city area in Manchester. The task basically involved three phases of analysis. The first was a historical narrative that attempted to weigh the evolution of the Hulme regeneration processes between 1960 and 1990 against the characteristic features of complex systems, with the aim of establishing a case for conceptualisation of urban regeneration as a subject of complexity. Using selected analytical tools of social network analysis, the second phase sought to quantify the regeneration networks of Hulme so as to weigh them against the deprivation indices for the area between 1990 and 2000, with the aim of testing for any correlations and their implications in the complexity project. Though equally facilitated by social network analysis, the third level was more concerned about investigating the enabling environment for the evolution of urban regeneration networks than mere quantification of the network parameters. Put together, the three levels of analysis provided a framework that serves as a fundamental analytical framework for urban regeneration processes. It offers a much more robust, emergent based, holistic approach to urban regeneration than that which iscontained in many of the contemporary claims of holism. The study's emphasis on intervention without violation of natural (social) order does not only provide a (potentially) essential tool for analysis but also sheds light on questions of the appropriate institutional thickness that is desirable for innovation. Being an exploratory undertaking, the study does not purport to be an exhaustive account of the issues raised, especially that complexity theory is itself still an emerging phenomenon

    Mapping biological ideas: Concept maps as knowledge integration tools for evolution education

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    Many students leave school with a fragmented understanding of biology that does not allow them to connect their ideas to their everyday lives (Wandersee, 1989; Mintzes, Wandersee, & Novak, 1998; Mintzes, Wandersee, & Novak, 2000a). Understanding evolution ideas is seen as central to building an integrated knowledge of biology (Blackwell, Powell, & Dukes, 2003; Thagard & Findlay, 2010). However, the theory of evolution has been found difficult to understand as it incorporates a wide range of ideas from different areas (Bahar et al., 1999; Tsui & Treagust, 2003) and multiple interacting levels (Wilensky & Resnick, 1999; Duncan & Reiser, 2007; Hmelo-Silver et al., 2007). Research suggests that learners can hold a rich repertoire of co-existing alternative ideas of evolution (for example, Bishop & Anderson, 1990; Demastes, Good, & Peebles, 1996; Evans, 2008), especially of human evolution (for example, Nelson, 1986; Sinatra et al., 2003; Poling & Evans, 2004). Evolution ideas are difficult to understand because they often contradict existing alternative ideas (Mayr, 1982; Wolpert, 1994; Evans, 2008). Research suggests that understanding human evolution is a key to evolution education (for example, Blackwell et al., 2003; Besterman & Baggott la Velle, 2007). This dissertation research investigates how different concept mapping forms embedded in a collaborative technology-enhanced learning environment can support students’ integration of evolution ideas using case studies of human evolution. Knowledge Integration (KI) (Linn et al., 2000; Linn et al., 2004) is used as the operational framework to explore concept maps as knowledge integration tools to elicit, add, critically distinguish, group, connect, and sort out alternative evolution ideas. Concept maps are a form of node-link diagram for organizing and representing connections between ideas as a semantic network (Novak & Gowin, 1984). This dissertation research describes the iterative development of a novel biology-specific form of concept map, called Knowledge Integration Map (KIM), which aims to help learners connect ideas across levels (for example, genotype and phenotype levels) towards an integrated understanding of evolution. Using a design-based research approach (Brown, 1992; Cobb et al., 2003), three iterative studies were implemented in ethnically and economically diverse public high schools classrooms using the web-based inquiry science environment (WISE) (Linn et al., 2003; Linn et al., 2004). Study 1 investigates concept maps as generative assessment tools. Study 1A compares the concept map generation and critique process of biology novices and experts. Findings suggest that concept maps are sensitive to different levels of knowledge integration but require scaffolding and revision. Study 1B investigates the implementation of concept maps as summative assessment tools in a WISE evolution module. Results indicate that concept maps can reveal connections between students’ alternative ideas of evolution. Study 2 introduces KIMs as embedded collaborative learning tools. After generating KIMs, student dyads revise KIMs through two different critique activities (comparison against an expert or peer generated KIM). Findings indicate that different critique activities can promote the use of different criteria for critique. Results suggest that the combination of generating and critiquing KIMs can support integrating evolution ideas but can be time-consuming. As time in biology classrooms is limited, study 3 distinguishes the learning effects from either generating or critiquing KIMs as more time efficient embedded learning tools. Findings suggest that critiquing KIMs can be more time efficient than generating KIMs. Using KIMs that include common alternative ideas for critique activities can create genuine opportunities for students to critically reflect on new and existing ideas. Critiquing KIMs can encourage knowledge integration by fostering self-monitoring of students’ learning progress, identifying knowledge gaps, and distinguishing alternative evolution ideas. This dissertation research demonstrates that science instruction of complex topics, such as human evolution, can succeed through a combination of scaffolded inquiry activities using dynamic visualizations, explanation activities, and collaborative KIM activities. This research contributes to educational research and practice by describing ways to make KIMs effective and time efficient learning tools for evolution education. Supporting students’ building of a more coherent understanding of core ideas of biology can foster their life-long interest and learning of science
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