A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach

A framework for analyzing changes in health care lexicons and nomenclatures

Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management

BIOMEDICAL LANGUAGE UNDERSTANDING AND EXTRACTION (BLUE-TEXT): A MINIMAL SYNTACTIC, SEMANTIC METHOD

Clinical text understanding (CTU) is of interest to health informatics because critical clinical information frequently represented as unconstrained text in electronic health records are extensively used by human experts to guide clinical practice, decision making, and to document delivery of care, but are largely unusable by information systems for queries and computations. Recent initiatives advocating for translational research call for generation of technologies that can integrate structured clinical data with unstructured data, provide a unified interface to all data, and contextualize clinical information for reuse in multidisciplinary and collaborative environment envisioned by CTSA program. This implies that technologies for the processing and interpretation of clinical text should be evaluated not only in terms of their validity and reliability in their intended environment, but also in light of their interoperability, and ability to support information integration and contextualization in a distributed and dynamic environment. This vision adds a new layer of information representation requirements that needs to be accounted for when conceptualizing implementation or acquisition of clinical text processing tools and technologies for multidisciplinary research. On the other hand, electronic health records frequently contain unconstrained clinical text with high variability in use of terms and documentation practices, and without commitmentto grammatical or syntactic structure of the language (e.g. Triage notes, physician and nurse notes, chief complaints, etc). This hinders performance of natural language processing technologies which typically rely heavily on the syntax of language and grammatical structure of the text. This document introduces our method to transform unconstrained clinical text found in electronic health information systems to a formal (computationally understandable) representation that is suitable for querying, integration, contextualization and reuse, and is resilient to the grammatical and syntactic irregularities of the clinical text. We present our design rationale, method, and results of evaluation in processing chief complaints and triage notes from 8 different emergency departments in Houston Texas. At the end, we will discuss significance of our contribution in enabling use of clinical text in a practical bio-surveillance setting

Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences

Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin

Web Ontologies to Categorialy Structure Reality: Representations of Human Emotional, Cognitive, and Motivational Processes

This work presents a Web ontology for modeling and representation of the emotional, cognitive and motivational state of online learners, interacting with university systems for distance or blended education. The ontology is understood as a way to provide the required mechanisms to model reality and associate it to emotional responses, but without committing to a particular way of organizing these emotional responses. Knowledge representation for the contributed ontology is performed by using Web Ontology Language (OWL), a semantic web language designed to represent rich and complex knowledge about things, groups of things, and relations between things. OWL is a computational logic-based language such that computer programs can exploit knowledge expressed in OWL and also facilitates sharing and reusing knowledge using the global infrastructure of the Web. The proposed ontology has been tested in the field of Massive Open Online Courses (MOOCs) to check if it is capable of representing emotions and motivation of the students in this context of use.This work has been supported by the Basque Government (IT421-10 and IT722-13), the Gipuzkoa Council (FA-208/2014-B) and the University of the Basque Country (PPV12/09). It has also been supported by InDAGuS (Spanish Government TIN2012-37826-C02) and INSPIRES, the Polytechnic Institute of Research and Innovation in Sustainability, Universitat de Lleida, Spai

A semantic-driven framework for IT support of clinical laboratory standards

The clinical laboratory plays a critical role in the delivery of care within the healthcare system by providing services that support accurate and timely diagnosis of diseases. The clinical laboratory relies on standard operating procedures (SOP) to provide information and guidance on the laboratory procedures. To ensure an excellent standard of clinical laboratory services, SOPs need to be of high quality, and practitioners need to have easy access to information contained within the SOPs. However, we argue in this thesis that there is a lack of standardization within clinical laboratory SOPs, and machines and human practitioners have difficulties accessing or using the content of SOPs. This thesis proposes a solution to challenges regarding the representation and use of SOPs in clinical laboratories (see Chapter 1). The research work in this thesis is based on the most up-to-date technological, theoretical, and empirical approaches (see Chapter 2). Additionally, external researchers have already utilized the outcome of this research for various purposes (see Chapter 5). In this thesis, we present the SmartSOP framework, a semantic-driven framework, that supports the representation of clinical laboratory procedure concepts in a standardised format for use within software applications. The SmartSOP framework consists of three main components, the Ontology for Clinical Laboratory SOP (OCL-SOP), the translation engine that converts free text SOPs to a standardised format, and a mobile application to provide lab practitioners with easy access to SOPs (see Chapters 3 and 4). We used the design science approach for the execution of this research work

DETAILED CLINICAL MODELS AND THEIR RELATION WITH ELECTRONIC HEALTH RECORDS

Tesis por compendio[EN] Healthcare domain produces and consumes big quantities of people's health data. Although data exchange is the norm rather than the exception, being able to access to all patient data is still far from achieved. Current developments such as personal health records will introduce even more data and complexity to the Electronic Health Records (EHR). Achieving semantic interoperability is one of the biggest challenges to overcome in order to benefit from all the information contained in the distributed EHR. This requires that the semantics of the information can be understood by all involved parties. It has been stablished that three layers are needed to achieve semantic interoperability: Reference models, clinical models (archetypes), and clinical terminologies. As seen in the literature, information models (reference models and clinical models) are lacking methodologies and tools to improve EHR systems and to develop new systems that can be semantically interoperable. The purpose of this thesis is to provide methodologies and tools for advancing the use of archetypes in three different scenarios: - Archetype definition over specifications with no dual model architecture native support. Any EHR architecture that directly or indirectly has the notion of detailed clinical models (such as HL7 CDA templates) can be potentially used as a reference model for archetype definition. This allows transforming single-model architectures (which contain only a reference model) into dual-model architectures (reference model with archetypes). A set of methodologies and tools has been developed to support the definition of archetypes from multiple reference models. - Data transformation. A complete methodology and tools are proposed to deal with the transformation of legacy data into XML documents compliant with the archetype and the underlying reference model. If the reference model is a standard then the transformation is a standardization process. The methodologies and tools allow both the transformation of legacy data and the transformation of data between different EHR standards. - Automatic generation of implementation guides and reference materials from archetypes. A methodology for the automatic generation of a set of reference materials is provided. These materials are useful for the development and use of EHR systems. These reference materials include data validators, example instances, implementation guides, human-readable formal rules, sample forms, mindmaps, etc. These reference materials can be combined and organized in different ways to adapt to different types of users (clinical or information technology staff). This way, users can include the detailed clinical model in their organization workflow and cooperate in the model definition. These methodologies and tools put clinical models as a key part of the system. The set of presented methodologies and tools ease the achievement of semantic interoperability by providing means for the semantic description, normalization, and validation of existing and new systems.[ES] El sector sanitario produce y consume una gran cantidad de datos sobre la salud de las personas. La necesidad de intercambiar esta información es una norma más que una excepción, aunque este objetivo está lejos de ser alcanzado. Actualmente estamos viviendo avances como la medicina personalizada que incrementarán aún más el tamaño y complejidad de la Historia Clínica Electrónica (HCE). La consecución de altos grados de interoperabilidad semántica es uno de los principales retos para aprovechar al máximo toda la información contenida en las HCEs. Esto a su vez requiere una representación fiel de la información de tal forma que asegure la consistencia de su significado entre todos los agentes involucrados. Actualmente está reconocido que para la representación del significado clínico necesitamos tres tipos de artefactos: modelos de referencia, modelos clínicos (arquetipos) y terminologías. En el caso concreto de los modelos de información (modelos de referencia y modelos clínicos) se observa en la literatura una falta de metodologías y herramientas que faciliten su uso tanto para la mejora de sistemas de HCE ya existentes como en el desarrollo de nuevos sistemas con altos niveles de interoperabilidad semántica. Esta tesis tiene como propósito proporcionar metodologías y herramientas para el uso avanzado de arquetipos en tres escenarios diferentes: - Definición de arquetipos sobre especificaciones sin soporte nativo al modelo dual. Cualquier arquitectura de HCE que posea directa o indirectamente la noción de modelos clínicos detallados (por ejemplo, las plantillas en HL7 CDA) puede ser potencialmente usada como modelo de referencia para la definición de arquetipos. Con esto se consigue transformar arquitecturas de HCE de modelo único (solo con modelo de referencia) en arquitecturas de doble modelo (modelo de referencia + arquetipos). Se han desarrollado metodologías y herramientas que faciliten a los editores de arquetipos el soporte a múltiples modelos de referencia. - Transformación de datos. Se propone una metodología y herramientas para la transformación de datos ya existentes a documentos XML conformes con los arquetipos y el modelo de referencia subyacente. Si el modelo de referencia es un estándar entonces la transformación será un proceso de estandarización de datos. La metodología y herramientas permiten tanto la transformación de datos no estandarizados como la transformación de datos entre diferentes estándares. - Generación automática de guías de implementación y artefactos procesables a partir de arquetipos. Se aporta una metodología para la generación automática de un conjunto de materiales de referencia de utilidad en el desarrollo y uso de sistemas de HCE, concretamente validadores de datos, instancias de ejemplo, guías de implementación , reglas formales legibles por humanos, formularios de ejemplo, mindmaps, etc. Estos materiales pueden ser combinados y organizados de diferentes modos para facilitar que los diferentes tipos de usuarios (clínicos, técnicos) puedan incluir los modelos clínicos detallados en el flujo de trabajo de su sistema y colaborar en su definición. Estas metodologías y herramientas ponen los modelos clínicos como una parte clave en el sistema. El conjunto de las metodologías y herramientas presentadas facilitan la consecución de la interoperabilidad semántica al proveer medios para la descripción semántica, normalización y validación tanto de sistemas nuevos como ya existentes.[CA] El sector sanitari produeix i consumeix una gran quantitat de dades sobre la salut de les persones. La necessitat d'intercanviar aquesta informació és una norma més que una excepció, encara que aquest objectiu està lluny de ser aconseguit. Actualment estem vivint avanços com la medicina personalitzada que incrementaran encara més la grandària i complexitat de la Història Clínica Electrònica (HCE). La consecució d'alts graus d'interoperabilitat semàntica és un dels principals reptes per a aprofitar al màxim tota la informació continguda en les HCEs. Açò, per la seua banda, requereix una representació fidel de la informació de tal forma que assegure la consistència del seu significat entre tots els agents involucrats. Actualment està reconegut que per a la representació del significat clínic necessitem tres tipus d'artefactes: models de referència, models clínics (arquetips) i terminologies. En el cas concret dels models d'informació (models de referència i models clínics) s'observa en la literatura una mancança de metodologies i eines que en faciliten l'ús tant per a la millora de sistemes de HCE ja existents com per al desenvolupament de nous sistemes amb alts nivells d'interoperabilitat semàntica. Aquesta tesi té com a propòsit proporcionar metodologies i eines per a l'ús avançat d'arquetips en tres escenaris diferents: - Definició d'arquetips sobre especificacions sense suport natiu al model dual. Qualsevol arquitectura de HCE que posseïsca directa o indirectament la noció de models clínics detallats (per exemple, les plantilles en HL7 CDA) pot ser potencialment usada com a model de referència per a la definició d'arquetips. Amb açò s'aconsegueix transformar arquitectures de HCE de model únic (solament amb model de referència) en arquitectures de doble model (model de referència + arquetips). S'han desenvolupat metodologies i eines que faciliten als editors d'arquetips el suport a múltiples models de referència. - Transformació de dades. Es proposa una metodologia i eines per a la transformació de dades ja existents a documents XML conformes amb els arquetips i el model de referència subjacent. Si el model de referència és un estàndard llavors la transformació serà un procés d'estandardització de dades. La metodologia i eines permeten tant la transformació de dades no estandarditzades com la transformació de dades entre diferents estàndards. - Generació automàtica de guies d'implementació i artefactes processables a partir d'arquetips. S'hi inclou una metodologia per a la generació automàtica d'un conjunt de materials de referència d'utilitat en el desenvolupament i ús de sistemes de HCE, concretament validadors de dades, instàncies d'exemple, guies d'implementació, regles formals llegibles per humans, formularis d'exemple, mapes mentals, etc. Aquests materials poden ser combinats i organitzats de diferents maneres per a facilitar que els diferents tipus d'usuaris (clínics, tècnics) puguen incloure els models clínics detallats en el flux de treball del seu sistema i col·laborar en la seua definició. Aquestes metodologies i eines posen els models clínics com una part clau del sistemes. El conjunt de les metodologies i eines presentades faciliten la consecució de la interoperabilitat semàntica en proveir mitjans per a la seua descripció semàntica, normalització i validació tant de sistemes nous com ja existents.Boscá Tomás, D. (2016). DETAILED CLINICAL MODELS AND THEIR RELATION WITH ELECTRONIC HEALTH RECORDS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/62174TESISCompendi

An ontological framework for the formal representation and management of human stress knowledge

There is a great deal of information on the topic of human stress which is embedded within numerous papers across various databases. However, this information is stored, retrieved, and used often discretely and dispersedly. As a result, discovery and identification of the links and interrelatedness between different aspects of knowledge on stress is difficult. This restricts the effective search and retrieval of desired information. There is a need to organize this knowledge under a unifying framework, linking and analysing it in mutual combinations so that we can obtain an inclusive view of the related phenomena and new knowledge can emerge. Furthermore, there is a need to establish evidence-based and evolving relationships between the ontology concepts.Previous efforts to classify and organize stress-related phenomena have not been sufficiently inclusive and none of them has considered the use of ontology as an effective facilitating tool for the abovementioned issues.There have also been some research works on the evolution and refinement of ontology concepts and relationships. However, these fail to provide any proposals for an automatic and systematic methodology with the capacity to establish evidence-based/evolving ontology relationships.In response to these needs, we have developed the Human Stress Ontology (HSO), a formal framework which specifies, organizes, and represents the domain knowledge of human stress. This machine-readable knowledge model is likely to help researchers and clinicians find theoretical relationships between different concepts, resulting in a better understanding of the human stress domain and its related areas. The HSO is formalized using OWL language and Protégé tool.With respect to the evolution and evidentiality of ontology relationships in the HSO and other scientific ontologies, we have proposed the Evidence-Based Evolving Ontology (EBEO), a methodology for the refinement and evolution of ontology relationships based on the evidence gleaned from scientific literature. The EBEO is based on the implementation of a Fuzzy Inference System (FIS).Our evaluation results showed that almost all stress-related concepts of the sample articles can be placed under one or more category of the HSO. Nevertheless, there were a number of limitations in this work which need to be addressed in future undertakings.The developed ontology has the potential to be used for different data integration and interoperation purposes in the domain of human stress. It can also be regarded as a foundation for the future development of semantic search engines in the stress domain

The National Transport Data Framework

Report by Professor Peter Landshoff (Cambridge University) and Professor John Polak (Imperial College London) on a project for the Department for Transport. emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions into a central catalogue, so that people can search for data and find data and understand their characteristics. The value of this is to individuals, to commercial organizations, and to public bodies. For example, services that provide better information to travellers will help to make their journey less stressful and persuade them to make more use of public transport. Transport operators need very diverse information to help them plan developments to their services: demographic, geographical, economic etc. And policy makers need a similar range of information to help them decide how to divide their budget and afterwards to evaluate how valuable it has been.This work was supported by the Department for Transport (DfT)