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

Design and Development of a Linked Open Data-Based Health Information Representation and Visualization System: Potentials and Preliminary Evaluation

Background: Healthcare organizations around the world are challenged by pressures to reduce cost, improve coordination and outcome, and provide more with less. This requires effective planning and evidence-based practice by generating important information from available data. Thus, flexible and user-friendly ways to represent, query, and visualize health data becomes increasingly important. International organizations such as the World Health Organization (WHO) regularly publish vital data on priority health topics that can be utilized for public health policy and health service development. However, the data in most portals is displayed in either Excel or PDF formats, which makes information discovery and reuse difficult. Linked Open Data (LOD)—a new Semantic Web set of best practice of standards to publish and link heterogeneous data—can be applied to the representation and management of public level health data to alleviate such challenges. However, the technologies behind building LOD systems and their effectiveness for health data are yet to be assessed. Objective: The objective of this study is to evaluate whether Linked Data technologies are potential options for health information representation, visualization, and retrieval systems development and to identify the available tools and methodologies to build Linked Data-based health information systems. Methods: We used the Resource Description Framework (RDF) for data representation, Fuseki triple store for data storage, and Sgvizler for information visualization. Additionally, we integrated SPARQL query interface for interacting with the data. We primarily use the WHO health observatory dataset to test the system. All the data were represented using RDF and interlinked with other related datasets on the Web of Data using Silk—a link discovery framework for Web of Data. A preliminary usability assessment was conducted following the System Usability Scale (SUS) method. Results: We developed an LOD-based health information representation, querying, and visualization system by using Linked Data tools. We imported more than 20,000 HIV-related data elements on mortality, prevalence, incidence, and related variables, which are freely available from the WHO global health observatory database. Additionally, we automatically linked 5312 data elements from DBpedia, Bio2RDF, and LinkedCT using the Silk framework. The system users can retrieve and visualize health information according to their interests. For users who are not familiar with SPARQL queries, we integrated a Linked Data search engine interface to search and browse the data. We used the system to represent and store the data, facilitating flexible queries and different kinds of visualizations. The preliminary user evaluation score by public health data managers and users was 82 on the SUS usability measurement scale. The need to write queries in the interface was the main reported difficulty of LOD-based systems to the end user. Conclusions: The system introduced in this article shows that current LOD technologies are a promising alternative to represent heterogeneous health data in a flexible and reusable manner so that they can serve intelligent queries, and ultimately support decision-making. However, the development of advanced text-based search engines is necessary to increase its usability especially for nontechnical users. Further research with large data sets is recommended in the future to unfold the potential of Linked Data and Semantic Web for future health information systems development

Propelling the Potential of Enterprise Linked Data in Austria. Roadmap and Report

In times of digital transformation and considering the potential of the data-driven economy, it is crucial that data is not only made available, data sources can be trusted, but also data integrity can be guaranteed, necessary privacy and security mechanisms are in place, and data and access comply with policies and legislation. In many cases, complex and interdisciplinary questions cannot be answered by a single dataset and thus it is necessary to combine data from multiple disparate sources. However, because most data today is locked up in isolated silos, data cannot be used to its fullest potential. The core challenge for most organisations and enterprises in regards to data exchange and integration is to be able to combine data from internal and external data sources in a manner that supports both day to day operations and innovation. Linked Data is a promising data publishing and integration paradigm that builds upon standard web technologies. It supports the publishing of structured data in a semantically explicit and interlinked manner such that it can be easily connected, and consequently becomes more interoperable and useful. The PROPEL project - Propelling the Potential of Enterprise Linked Data in Austria - surveyed technological challenges, entrepreneurial opportunities, and open research questions on the use of Linked Data in a business context and developed a roadmap and a set of recommendations for policy makers, industry, and the research community. Shifting away from a predominantly academic perspective and an exclusive focus on open data, the project looked at Linked Data as an emerging disruptive technology that enables efficient enterprise data management in the rising data economy. Current market forces provide many opportunities, but also present several data and information management challenges. Given that Linked Data enables advanced analytics and decision-making, it is particularly suitable for addressing today's data and information management challenges. In our research, we identified a variety of highly promising use cases for Linked Data in an enterprise context. Examples of promising application domains include "customization and customer relationship management", "automatic and dynamic content production, adaption and display", "data search, information retrieval and knowledge discovery", as well as "data and information exchange and integration". The analysis also revealed broad potential across a large spectrum of industries whose structural and technological characteristics align well with Linked Data characteristics and principles: energy, retail, finance and insurance, government, health, transport and logistics, telecommunications, media, tourism, engineering, and research and development rank among the most promising industries for the adoption of Linked Data principles. In addition to approaching the subject from an industry perspective, we also examined the topics and trends emerging from the research community in the field of Linked Data and the Semantic Web. Although our analysis revolved around a vibrant and active community composed of academia and leading companies involved in semantic technologies, we found that industry needs and research discussions are somewhat misaligned. Whereas some foundation technologies such as knowledge representation and data creation/publishing/sharing, data management and system engineering are highly represented in scientific papers, specific topics such as recommendations, or cross-topics such as machine learning or privacy and security are marginally present. Topics such as big/large data and the internet of things are (still) on an upward trajectory in terms of attention. In contrast, topics that are very relevant for industry such as application oriented topics or those that relate to security, privacy and robustness are not attracting much attention. When it comes to standardisation efforts, we identified a clear need for a more in-depth analysis into the effectiveness of existing standards, the degree of coverage they provide with respect the foundations they belong to, and the suitability of alternative standards that do not fall under the core Semantic Web umbrella. Taking into consideration market forces, sector analysis of Linked Data potential, demand side analysis and the current technological status it is clear that Linked Data has a lot of potential for enterprises and can act as a key driver of technological, organizational, and economic change. However, in order to ensure a solid foundation for Enterprise Linked Data include there is a need for: greater awareness surrounding the potential of Linked Data in enterprises, lowering of entrance barriers via education and training, better alignment between industry demands and research activities, greater support for technology transfer from universities to companies. The PROPEL roadmap recommends concrete measures in order to propel the adoption of Linked Data in Austrian enterprises. These measures are structured around five fields of activities: "awareness and education", "technological innovation, research gaps, standardisation", "policy and legal", and "funding". Key short-term recommendations include the clustering of existing activities in order to raise visibility on an international level, the funding of key topics that are under represented by the community, and the setup of joint projects. In the medium term, we recommend the strengthening of existing academic and private education efforts via certification and to establish flagship projects that are based on national use cases that can serve as blueprints for transnational initiatives. This requires not only financial support, but also infrastructure support, such as data and services to build solutions on top. In the long term, we recommend cooperation with international funding schemes to establish and foster a European level agenda, and the setup of centres of excellence

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 difícil su integración. En este contexto, el paradigma de los Datos Vinculados como una tecnología 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 tecnología, 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 metodología automática para la clasificación de enfermedades de pacientes y su posterior validación haciendo uso de un razonador OWL

Global integration of public sector information

This paper deals with technological methods for consolidating assets lists of available public sector information (PSI) for re-use. In this direction, the effort is to review the state of the art in delivering access to PSI throughout the world and to prioritize the necessary engagements for joining available PSI catalogues. We propose an architectural framework grounded on Semantic Web technologies to deliver a global platform for federated searching. A speculative survey of available PSI portals is presented, and the initial implementation, results, and analysis of the proposed architecture are covered in detail

Integrating public datasets using linked data: challenges and design principles

The world is moving from a state where there is paucity of data to one of surfeit. These data, and datasets, are normally in different datastores and of different formats. Connecting these datasets together will increase their value and help discover interesting relationships amongst them. This paper describes our experience of using Linked Data to inter-operate these different datasets, the challenges we faced, and the solutions we devised. The paper concludes with apposite design principles for using linked data to inter-operate disparate datasets

Report on the EHCR (Deliverable 26.2)

This deliverable is the second for Workpackage 26. The first, submitted after Month 12, summarised the areas of research that the partners had identified as being relevant to the semantic indexing of the EHR. This second one reports progress on the key threads of work identified by the partners during the project to contribute towards semantically interoperable and processable EHRs. This report provides a set of short summaries on key topics that have emerged as important, and to which the partners are able to make strong contributions. Some of these are also being extended via two new EU Framework 6 proposals that include WP26 partners: this is also a measure of the success of this Network of Excellence

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770