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The Corpus Expansion Toolkit: finding what we want on the web
This thesis presents the Corpus Expansion Toolkit (CET), a generally applicable toolkit that allows researchers to build domain-specific corpora from the web. The main purpose of the work presented in this thesis and the development of the CET is to provide a solution to discovering desired content on the web from possibly unknown locations or a poorly defined domain. Using an iterative process, the CET is able to solve the problem of discovering domain-specific online content and expand a corpus using only a very small number of example documents or characteristic phrases taken from the target domain. Using a human-in-the-loop strategy and a chain of discrete software components the CET also allows the concept of a domain to be iteratively defined using the very online resources used to expand the original corpus. The CET combines feature extraction, search, web crawling and machine learning methods to collected, store, filter and perform information extraction on collected documents. Using a small number of example ‘seed’ documents the CET is able to expand the original corpus by finding more relevant documents from the web and provide a number of tools to support their analysis. This thesis presents a case study-based methodology that introduces the various contributions and components of the CET through the discussion of five case studies covering a wide variety of domains and requirements that the CET has been applied. These case studies hope to illustrate three main use cases, listed below, where the CET is applicable:
1. Domain known – source known
2. Domain known – source unknown
3. Domain unknown – source unknown
First, use cases where the sites for document collection are known and the topic of research is clearly defined. Second, instances where the topic of research is clearly defined but where to find relevant documents on the web is unknown. Third, the most extreme use case, where the domain is poorly defined or unknown to the researcher and the location of the information is also unknown. This thesis presents a solution that allows researchers to begin with very little information on a specific topic and iteratively build a clear conception of a domain and translate that to a computational system
Social media mining for identification and exploration of health-related information from pregnant women
Widespread use of social media has led to the generation of substantial
amounts of information about individuals, including health-related information.
Social media provides the opportunity to study health-related information about
selected population groups who may be of interest for a particular study. In
this paper, we explore the possibility of utilizing social media to perform
targeted data collection and analysis from a particular population group --
pregnant women. We hypothesize that we can use social media to identify cohorts
of pregnant women and follow them over time to analyze crucial health-related
information. To identify potentially pregnant women, we employ simple
rule-based searches that attempt to detect pregnancy announcements with
moderate precision. To further filter out false positives and noise, we employ
a supervised classifier using a small number of hand-annotated data. We then
collect their posts over time to create longitudinal health timelines and
attempt to divide the timelines into different pregnancy trimesters. Finally,
we assess the usefulness of the timelines by performing a preliminary analysis
to estimate drug intake patterns of our cohort at different trimesters. Our
rule-based cohort identification technique collected 53,820 users over thirty
months from Twitter. Our pregnancy announcement classification technique
achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user
timelines. Analysis of the timelines revealed that pertinent health-related
information, such as drug-intake and adverse reactions can be mined from the
data. Our approach to using user timelines in this fashion has produced very
encouraging results and can be employed for other important tasks where
cohorts, for which health-related information may not be available from other
sources, are required to be followed over time to derive population-based
estimates.Comment: 9 page
Linked Data based Health Information Representation, Visualization and Retrieval System on the Semantic Web
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.To better facilitate health information dissemination, using flexible ways to
represent, query and visualize health data becomes increasingly important.
Semantic Web technologies, which provide a common framework by
allowing data to be shared and reused between applications, can be applied
to the management of health data. Linked open data - a new semantic web
standard to publish and link heterogonous data- allows not only human,
but also machine to brows data in unlimited way.
Through a use case of world health organization HIV data of sub Saharan
Africa - which is severely affected by HIV epidemic, this thesis built a
linked data based health information representation, querying and
visualization system. All the data was represented with RDF, by
interlinking it with other related datasets, which are already on the cloud.
Over all, the system have more than 21,000 triples with a SPARQL
endpoint; where users can download and use the data and – a SPARQL
query interface where users can put different type of query and retrieve the
result. Additionally, It has also a visualization interface where users can
visualize the SPARQL result with a tool of their preference. For users who
are not familiar with SPARQL queries, they can use the linked data search
engine interface to search and browse the data.
From this system we can depict that current linked open data technologies
have a big potential to represent heterogonous health data in a flexible and
reusable manner and they can serve in intelligent queries, which can
support decision-making. However, in order to get the best from these
technologies, improvements are needed both at the level of triple stores
performance and domain-specific ontological vocabularies
Web Tracking: Mechanisms, Implications, and Defenses
This articles surveys the existing literature on the methods currently used
by web services to track the user online as well as their purposes,
implications, and possible user's defenses. A significant majority of reviewed
articles and web resources are from years 2012-2014. Privacy seems to be the
Achilles' heel of today's web. Web services make continuous efforts to obtain
as much information as they can about the things we search, the sites we visit,
the people with who we contact, and the products we buy. Tracking is usually
performed for commercial purposes. We present 5 main groups of methods used for
user tracking, which are based on sessions, client storage, client cache,
fingerprinting, or yet other approaches. A special focus is placed on
mechanisms that use web caches, operational caches, and fingerprinting, as they
are usually very rich in terms of using various creative methodologies. We also
show how the users can be identified on the web and associated with their real
names, e-mail addresses, phone numbers, or even street addresses. We show why
tracking is being used and its possible implications for the users (price
discrimination, assessing financial credibility, determining insurance
coverage, government surveillance, and identity theft). For each of the
tracking methods, we present possible defenses. Apart from describing the
methods and tools used for keeping the personal data away from being tracked,
we also present several tools that were used for research purposes - their main
goal is to discover how and by which entity the users are being tracked on
their desktop computers or smartphones, provide this information to the users,
and visualize it in an accessible and easy to follow way. Finally, we present
the currently proposed future approaches to track the user and show that they
can potentially pose significant threats to the users' privacy.Comment: 29 pages, 212 reference
Predicting Medication Prescription Rankings with Medication Relation Network
Medication prescription rankings and demands prediction could benefit both medication consumers and pharmaceutical companies from various aspects. Our study predicts the medication prescription rankings focusing on patients’ medication switch and combination behavior, which is an innovative genre of medication knowledge that could be learned from unstructured patient generated contents. We first construct two supervised machine learning systems for medication references identification and medication relations classification from unstructured patient’s reviews. We further map the medication switch and combination relations into directed and undirected networks respectively. An adjusted transition in and out (ATIO) system is proposed for medication prescription rankings prediction. The proposed system demonstrates the highest positive correlation with actual medication prescription amounts comparing to other network-based measures. In order to predict the prescription demand changes, we compare four predictive regression models. The model incorporated the network-based measure from ATIO system achieve the lowest mean square errors
Application of Semantics to Solve Problems in Life Sciences
Fecha de lectura de Tesis: 10 de diciembre de 2018La cantidad de información que se genera en la Web se ha incrementado en los últimos años. La mayor parte de esta información se encuentra accesible en texto, siendo el ser humano el principal usuario de la Web. Sin embargo, a pesar de todos los avances producidos en el área del procesamiento del lenguaje natural, los ordenadores tienen problemas para procesar esta información textual. En este cotexto, existen dominios de aplicación en los que se están publicando grandes cantidades de información disponible como datos estructurados como en el área de las Ciencias de la Vida. El análisis de estos datos es de vital importancia no sólo para el avance de la ciencia, sino para producir avances en el ámbito de la salud. Sin embargo, estos datos están localizados en diferentes repositorios y almacenados en diferentes formatos que hacen difÃcil su integración. En este contexto, el paradigma de los Datos Vinculados como una tecnologÃa que incluye la aplicación de algunos estándares propuestos por la comunidad W3C tales como HTTP URIs, los estándares RDF y OWL. Haciendo uso de esta tecnologÃa, se ha desarrollado esta tesis doctoral basada en cubrir los siguientes objetivos principales: 1) promover el uso de los datos vinculados por parte de la comunidad de usuarios del ámbito de las Ciencias de la Vida 2) facilitar el diseño de consultas SPARQL mediante el descubrimiento del modelo subyacente en los repositorios RDF 3) crear un entorno colaborativo que facilite el consumo de Datos Vinculados por usuarios finales, 4) desarrollar un algoritmo que, de forma automática, permita descubrir el modelo semántico en OWL de un repositorio RDF, 5) desarrollar una representación en OWL de ICD-10-CM llamada Dione que ofrezca una metodologÃa automática para la clasificación de enfermedades de pacientes y su posterior validación haciendo uso de un razonador OWL
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