Semantic network analysis of ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

Dataflow Computation for the J-Machine

The dataflow model of computation exposes and exploits parallelism in programs without requiring programmer annotation; however, instruction- level dataflow is too fine-grained to be efficient on general-purpose processors. A popular solution is to develop a "hybrid'' model of computation where regions of dataflow graphs are combined into sequential blocks of code. I have implemented such a system to allow the J-Machine to run Id programs, leaving exposed a high amount of parallelism --- such as among loop iterations. I describe this system and provide an analysis of its strengths and weaknesses and those of the J-Machine, along with ideas for improvement.

Neural Networks

We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view learning algorithms as methods for finding parameter values that look probable in the light of the data. We discuss basic issues in representation and learning, and treat some of the practical issues that arise in fitting networks to data. We also discuss links between neural networks and the general formalism of graphical models.

Evaluación y mejoramiento productivo de PLASPUCOL

Este proyecto de grado pretende evaluar el comportamiento productivo de la empresa Plaspucol ubicada en la ciudad de Bogotá, identificando las deficiencias en su proceso y generando mecanismo de mejoramiento a través de un previo análisis. Para ello es necesario partir de un marco teórico del plástico como lo es su historia, su evolución, clasificación y su posicionamiento e influencia económica a nivel mundial y vista a su vez desde el ámbito nacional. Para analizar dichas situaciones se usaron herramientas aprendidas en lo largo de nuestra formación profesional como los diagramas de recorrido, diagramas de flujo, diagrama hombre-máquina, diagrama de balanceo, muestreo y un moderno simulador llamado Promodel con el cual se diagnostica a la empresa identificando puntos débiles y cuellos de botella en la producción en la situación actual y se crea una situación futura con propuestas de mejora empleando éste simulador.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 5: Affiliation Networks

How can we analyze and understand affiliation networks? In this class, we will discuss properties of affiliation networks and we will investigate the use of Galois lattices for the exploration of structural patterns in bi-partite graphs. Optional : L.C. Freeman and D.R. White. Using Galois Lattices to Represent Network Data. Sociological Methodology, (23):127--146, (1993)

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 4: Social Network Analysis

What are fundamental entities in social networks and what information is contained in social graphs? We will discuss some selected concepts in social network analysis, such as one- and two mode networks, prestige and centrality, and cliques, clans and clubs. Readings: Web tool predicts election results and stock prices, J. Palmer, New Scientist, 07 February (2008) [Protected Access] Optional: Social Network Analysis, Methods and Applications, S. Wasserman and K. Faust (1994)

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 7: Link Analysis and Search

What are ways of searching in graphs? In this class, we will discuss basics of link analysis, including Google's PageRank algorithm as an example. Readings: The PageRank Citation Ranking: Bringing Order to the Web, L. Page and S. Brin and R. Motwani and T. Winograd (1998) Stanford Tecnical Report

Introduction to Matlab - a self-study tutorial

Matlab is a high level language that is very easy to use and very powerful. It comes with a wealth of libraries and toolboxes, that you can use directly, so that you don't need to program low level functions. It enables you to display results very easily on graphs and images. To get started with it, you need to understand how to manipulate and represent data, and how to find information about the available functions. During this self-study tutorial, you will learn: 1- How to start Matlab. 2- How you can find out all the information you need. 3- How to create simple vectors and matrices. 4- What functions are available and how to find them. 5- How to plot graphs of functions. 6- How to write a script. After this (should take about an hour), you will know most of what you need to know about Matlab and should definitely know how to go on learning about it on your own…

Matlab self study tutorial - Graphing

These resources are designed to support students in gaining more confidence with using Matlab. The PDFs provide guidance and information; Objectives: Introduce basic syntax and data preparation for graphing with Matlab by providing some data, examples of code and some background documents. Outcomes: -how to write an m file script -the importance of syntax -how to load files -how to produce simple graphs -where to get help and further examples There are also some data files to provide example data for students to work with in producing Matlab resources.

Social Networks and Small World phenomena

Social Networking tools like Facebook yield recognisable small world phenomena, that is particular kinds of social graphs that facilitate particular kinds of interaction and information exchange.

Lecture notes

Lecture notes in LaTex

Sostenibilidad del proyecto industrial del plástico .

El reciclaje se ha caracterizado por ser un tema importante en la última década, por el desarrollo económico, social y tecnológico que tiene consigo. Evidentemente, el sector de reciclaje se ha convertido en un sector con visión para poder conseguir un nuevo sector. Por este motivo lo que se ha querido con este trabajo de investigación es buscar nuevas manera de ver los recursos que se encuentran en cualquier lugar de las ciudades. La tesis cuenta con un sin número de argumentos que ayudarán a que las personas que lean el documento se interesen cada vez más en reutilizar los materiales que día a día encuentran. De esta manera, se puede ver una cadena de suministro, que llevará a que la materia prima que se haya mejorado sirva para realizar otro tipo de productos y genere un sostenimiento significativo a miles de personas que pueden sacarle provecho a estos materiales. Nuestros hábitos más cotidianos tienen mucho que ver con la degradación global del planeta. Actos tan rutinarios como tirar la basura sin separarla, comprar utensilios de usar y tirar o adquirir los alimentos envasados en materiales anti ecológicos o no reciclables contribuyen en gran medida a la contaminación medioambiental (Inzillo, 2000).

Lecture notes pdf

Lecture notes in PDF

WAIS seminar: Provenance Analytics and Crowdsourcing

Wednesday 26th March 2014 Speaker(s): Dr Trung Dong Huynh Organiser: Dr Tim Chown Time: 26/03/2014 11:00-11:50 Location: B32/3077 File size: 349Mb Abstract Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. In this talk, I will present an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. I will also talk about CollabMap (www.collabmap.org), an online crowdsourcing mapping application, and show how we applied the approach above to the trust classification of data generated by the crowd, achieving an accuracy over 95%.

Presión arterial en preescolares de la localidad de Usaquén en Bogotá

Introducción: La hipertensión arterial es un problema de salud pública tanto en países industrializados como en vía de desarrollo. Su prevalencia en la infancia viene en aumento por lo que es relevante determinarla en niños preescolares a nivel local. Objetivo: Determinar la prevalencia de hipertensión arterial en niños de tres a cinco años de una cohorte de 14 hogares infantiles del ICBF de la localidad de Usaquén en Bogotá. Materiales y métodos: Se realizó un estudio de corte transversal analítico, utilizando la base de datos de un ensayo aleatorizado y controlado del año 200913, y se evaluaron las cifras de tensión arterial de acuerdo a sexo, edad, talla y su correlación con el IMC con un nivel de confianza del 95% y precisión del 1%. Se calcularon las medias, desviaciones estándar, percentiles y prevalencia. Resultados: Se obtuvo una muestra de 1035 casos, encontrándose una prevalencia de 4,5% de HTA sistólica, 10,4% de diastólica, ambas en estadio I; teniendo en cuenta tanto sistólica como diastólica, fue de 11,6% en estadio I. Se determinaron los valores de presión arterial sistólica y diastólica en cuartiles de acuerdo a edad, sexo y talla. El coeficiente de correlación entre el IMC y los niveles de presión arterial sistólica y diastólica fueron de 0.0992 y 0.0362 respectivamente. Conclusión: La prevalencia de HTA general fue de 11,6%, predominando la diastólica en estadio I en niños preescolares. No se encontró correlación entre el IMC y las cifras de tensión arterial sistólica y diastólica.