Documenting Data Integration Using Knowledge Graphs

With the increasing volume of data on the Web and the proliferation of published knowledge graphs, there is a growing need for improved data management and information extraction. However, heterogeneity issues across the data sources, i.e., various formats and systems, negatively impact efficient access, manage, reuse, and analyze the data. A data integration system (DIS) provides uniform access to heterogeneous data sources and their relationships; it offers a unified and comprehensive view of the data. DISs resort to mapping rules, expressed in declarative languages like RML, to align data from various sources to classes and properties defined in an ontology. This work defines a knowledge graph where data integration systems are represented as factual statements. The aim of this work is to provide the basis for integrated analysis of data collected from heterogeneous data silos. The proposed knowledge graph is also specified as a data integration system, that integrates all data integration systems. The proposed solution includes a unified schema, which defines and explains the relationships between all elements in the data integration system DIS=⟨G, S, M, F⟩. The results suggest that factual statements from the proposed knowledge graph, improve the understanding of the features that characterize knowledge graphs declaratively defined like data integration systems

Supporting Explainable AI on Semantic Constraint Validation

There is a rising number of knowledge graphs available published through various sources. The enormous amount of linked data strives to give entities a semantic context. Using SHACL, the entities can be validated with respect to their context. On the other hand, an increasing usage of AI models in productive systems comes with a great responsibility in various areas. Predictive models like linear, logistic regression, and tree-based models, are still frequently used as they come with a simple structure, which allows for interpretability. However, explaining models includes verifying whether the model makes predictions based on human constraints or scientific facts. This work proposes to use the semantic context of the entities in knowledge graphs to validate predictive models with respect to user-defined constraints; therefore, providing a theoretical framework for a model-agnostic validation engine based on SHACL. In a second step, the model validation results are summarized in the case of a decision tree and visualized model-coherently. Finally, the performance of the framework is evaluated based on a Python implementation

Knowledge Management approaches to model pathophysiological mechanisms and discover drug targets in Multiple Sclerosis

Multiple Sclerosis (MS) is one of the most prevalent neurodegenerative diseases for which a cure is not yet available. MS is a complex disease for numerous reasons; its etiology is unknown, the diagnosis is not exclusive, the disease course is unpredictable and therapeutic response varies from patient to patient. There are four established subtypes of MS, which are segregated based on different characteristics. Many environmental and genetic factors are considered to play a role in MS etiology, including viral infection, vitamin D deficiency, epigenetical changes and some genes. Despite the large body of diverse scientific knowledge, from laboratory findings to clinical trials, no integrated model which portrays the underlying mechanisms of the disease state of MS is available. Contemporary therapies only provide reduction in the severity of the disease, and there is an unmet need of efficient drugs. The present thesis provides a knowledge-based rationale to model MS disease mechanisms and identify potential drug candidates by using systems biology approaches. Systems biology is an emerging field which utilizes the computational methods to integrate datasets of various granularities and simulate the disease outcome. It provides a framework to model molecular dynamics with their precise interaction and contextual details. The proposed approaches were used to extract knowledge from literature by state of the art text mining technologies, integrate it with proprietary data using semantic platforms, and build different models (molecular interactions map, agent based models to simulate disease outcome, and MS disease progression model with respect to time). For better information representation, disease ontology was also developed and a methodology of automatic enrichment was derived. The models provide an insight into the disease, and several pathways were explored by combining the therapeutics and the disease-specific prescriptions. The approaches and models developed in this work resulted in the identification of novel drug candidates that are backed up by existing experimental and clinical knowledge

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

Computer vision-based structural assessment exploiting large volumes of images

Visual assessment is a process to understand the state of a structure based on evaluations originating from visual information. Recent advances in computer vision to explore new sensors, sensing platforms and high-performance computing have shed light on the potential for vision-based visual assessment in civil engineering structures. The use of low-cost, high-resolution visual sensors in conjunction with mobile and aerial platforms can overcome spatial and temporal limitations typically associated with other forms of sensing in civil structures. Also, GPU-accelerated and parallel computing offer unprecedented speed and performance, accelerating processing the collected visual data. However, despite the enormous endeavor in past research to implement such technologies, there are still many practical challenges to overcome to successfully apply these techniques in real world situations. A major challenge lies in dealing with a large volume of unordered and complex visual data, collected under uncontrolled circumstance (e.g. lighting, cluttered region, and variations in environmental conditions), while just a tiny fraction of them are useful for conducting actual assessment. Such difficulty induces an undesirable high rate of false-positive and false-negative errors, reducing the trustworthiness and efficiency of their implementation. To overcome the inherent challenges in using such images for visual assessment, high-level computer vision algorithms must be integrated with relevant prior knowledge and guidance, thus aiming to have similar performance with those of humans conducting visual assessment. Moreover, the techniques must be developed and validated in the realistic context of a large volume of real-world images, which is likely contain numerous practical challenges. In this dissertation, the novel use of computer vision algorithms is explored to address two promising applications of vision-based visual assessment in civil engineering: visual inspection, and visual data analysis for post-disaster evaluation. For both applications, powerful techniques are developed here to enable reliable and efficient visual assessment for civil structures and demonstrate them using a large volume of real-world images collected from actual structures. State-of-art computer vision techniques, such as structure-from-motion and convolutional neural network techniques, facilitate these tasks. The core techniques derived from this study are scalable and expandable to many other applications in vision-based visual assessment, and will serve to close the existing gaps between past research efforts and real-world implementations

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Knowledge Management Approaches for predicting Biomarker and Assessing its Impact on Clinical Trials

The recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for the drug discovery companies to invest into novel strategies for stratified biomarker discovery. Catching with this trend, trials with stratified biomarker in drug development have quadrupled in the last decade but represent a small part of all Interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics. To overcome the challenge, varied knowledge management and system biology approaches are adopted in the clinics to analyze/interpret an ever increasing collection of OMICS data. By semi-automatic screening of more than 150,000 trials, we filtered trials with stratified biomarker to analyse their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. The analysis clearly shows that cancer is the major focus for trials with stratified biomarker. But targeted therapies in cancer require more accurate stratification of patient population. This can be augmented by a fresh approach of selecting a new class of biomolecules i.e. miRNA as candidate stratification biomarker. miRNA plays an important role in tumorgenesis in regulating expression of oncogenes and tumor suppressors; thus affecting cell proliferation, differentiation, apoptosis, invasion, angiogenesis. miRNAs are potential biomarkers in different cancer. However, the relationship between response of cancer patients towards targeted therapy and resulting modifications of the miRNA transcriptome in pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have created an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. We present a novel SMARTmiR algorithm to predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer. The application of an optimised and fully automated version of the algorithm has the potential to be used as clinical decision support tool. Moreover this research will also provide a comprehensive and valuable knowledge map demonstrating functional bimolecular interactions in colorectal cancer to scientific community. This research also detected seven miRNA i.e. hsa-miR-145, has-miR-27a, has- miR-155, hsa-miR-182, hsa-miR-15a, hsa-miR-96 and hsa-miR-106a as top stratified biomarker candidate for cetuximab therapy in CRC which were not reported previously. Finally a prospective plan on future scenario of biomarker research in cancer drug development has been drawn focusing to reduce the risk of most expensive phase III drug failures

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Towards Efficient Novel Materials Discovery

Die Entdeckung von neuen Materialien mit speziellen funktionalen Eigenschaften ist eins der wichtigsten Ziele in den Materialwissenschaften. Das Screening des strukturellen und chemischen Phasenraums nach potentiellen neuen Materialkandidaten wird häufig durch den Einsatz von Hochdurchsatzmethoden erleichtert. Schnelle und genaue Berechnungen sind eins der Hauptwerkzeuge solcher Screenings, deren erster Schritt oft Geometrierelaxationen sind. In Teil I dieser Arbeit wird eine neue Methode der eingeschränkten Geometrierelaxation vorgestellt, welche die perfekte Symmetrie des Kristalls erhält, Resourcen spart sowie Relaxationen von metastabilen Phasen und Systemen mit lokalen Symmetrien und Verzerrungen erlaubt. Neben der Verbesserung solcher Berechnungen um den Materialraum schneller zu durchleuchten ist auch eine bessere Nutzung vorhandener Daten ein wichtiger Pfeiler zur Beschleunigung der Entdeckung neuer Materialien. Obwohl schon viele verschiedene Datenbanken für computerbasierte Materialdaten existieren ist die Nutzbarkeit abhängig von der Darstellung dieser Daten. Hier untersuchen wir inwiefern semantische Technologien und Graphdarstellungen die Annotation von Daten verbessern können. Verschiedene Ontologien und Wissensgraphen werden entwickelt anhand derer die semantische Darstellung von Kristallstrukturen, Materialeigenschaften sowie experimentellen Ergebenissen im Gebiet der heterogenen Katalyse ermöglicht werden. Wir diskutieren, wie der Ansatz Ontologien und Wissensgraphen zu separieren, zusammenbricht wenn neues Wissen mit künstlicher Intelligenz involviert ist. Eine Zwischenebene wird als Lösung vorgeschlagen. Die Ontologien bilden das Hintergrundwissen, welches als Grundlage von zukünftigen autonomen Agenten verwendet werden kann. Zusammenfassend ist es noch ein langer Weg bis Materialdaten für Maschinen verständlich gemacht werden können, so das der direkte Nutzen semantischer Technologien nach aktuellem Stand in den Materialwissenschaften sehr limitiert ist.The discovery of novel materials with specific functional properties is one of the highest goals in materials science. Screening the structural and chemical space for potential new material candidates is often facilitated by high-throughput methods. Fast and still precise computations are a main tool for such screenings and often start with a geometry relaxation to find the nearest low-energy configuration relative to the input structure. In part I of this work, a new constrained geometry relaxation is presented which maintains the perfect symmetry of a crystal, saves time and resources as well as enables relaxations of meta-stable phases and systems with local symmetries or distortions. Apart from improving such computations for a quicker screening of the materials space, better usage of existing data is another pillar that can accelerate novel materials discovery. While many different databases exists that make computational results accessible, their usability depends largely on how the data is presented. We here investigate how semantic technologies and graph representations can improve data annotation. A number of different ontologies and knowledge graphs are developed enabling the semantic representation of crystal structures, materials properties as well experimental results in the field of heterogeneous catalysis. We discuss the breakdown of the knowledge-graph approach when knowledge is created using artificial intelligence and propose an intermediate information layer. The underlying ontologies can provide background knowledge for possible autonomous intelligent agents in the future. We conclude that making materials science data understandable to machines is still a long way to go and the usefulness of semantic technologies in the domain of materials science is at the moment very limited

A Semantics-based User Interface Model for Content Annotation, Authoring and Exploration

The Semantic Web and Linked Data movements with the aim of creating, publishing and interconnecting machine readable information have gained traction in the last years. However, the majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way in which humans interact with information. Semantic structuring of content on the other hand provides a wide range of advantages compared to unstructured information. Semantically-enriched documents facilitate information search and retrieval, presentation, integration, reusability, interoperability and personalization. Looking at the life-cycle of semantic content on the Web of Data, we see quite some progress on the backend side in storing structured content or for linking data and schemata. Nevertheless, the currently least developed aspect of the semantic content life-cycle is from our point of view the user-friendly manual and semi-automatic creation of rich semantic content. In this thesis, we propose a semantics-based user interface model, which aims to reduce the complexity of underlying technologies for semantic enrichment of content by Web users. By surveying existing tools and approaches for semantic content authoring, we extracted a set of guidelines for designing efficient and effective semantic authoring user interfaces. We applied these guidelines to devise a semantics-based user interface model called WYSIWYM (What You See Is What You Mean) which enables integrated authoring, visualization and exploration of unstructured and (semi-)structured content. To assess the applicability of our proposed WYSIWYM model, we incorporated the model into four real-world use cases comprising two general and two domain-specific applications. These use cases address four aspects of the WYSIWYM implementation: 1) Its integration into existing user interfaces, 2) Utilizing it for lightweight text analytics to incentivize users, 3) Dealing with crowdsourcing of semi-structured e-learning content, 4) Incorporating it for authoring of semantic medical prescriptions