Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Narrative Information Extraction with Non-Linear Natural Language Processing Pipelines

Computational narrative focuses on methods to algorithmically analyze, model, and generate narratives. Most current work in story generation, drama management or even literature analysis relies on manually authoring domain knowledge in some specific formal representation language, which is expensive to generate. In this dissertation we explore how to automatically extract narrative information from unannotated natural language text, how to evaluate the extraction process, how to improve the extraction process, and how to use the extracted information in story generation applications. As our application domain, we use Vladimir Propp's narrative theory and the corresponding Russian and Slavic folktales as our corpus. Our hypothesis is that incorporating narrative-level domain knowledge (i.e., Proppian theory) to core natural language processing (NLP) and information extraction can improve the performance of tasks (such as coreference resolution), and the extracted narrative information. We devised a non-linear information extraction pipeline framework which we implemented in Voz, our narrative information extraction system. Finally, we studied how to map the output of Voz to an intermediate computational narrative model and use it as input for an existing story generation system, thus further connecting existing work in NLP and computational narrative. As far as we know, it is the first end-to-end computational narrative system that can automatically process a corpus of unannotated natural language stories, extract explicit domain knowledge from them, and use it to generate new stories. Our user study results show that specific error introduced during the information extraction process can be mitigated downstream and have virtually no effect on the perceived quality of the generated stories compared to generating stories using handcrafted domain knowledge.Ph.D., Computer Science -- Drexel University, 201

Integrating and querying linked datasets through ontological rules

The Web of Linked Open Data has developed from a few datasets in 2007 into a large data space containing billions of RDF triples published and stored in hundreds of independent datasets, so as to form the so called Linked Open Data Cloud. This information cloud, ranging over a wide set of data domains, poses a challenge when it comes to reconciling heterogeneous schemas or vocabularies adopted by data publishers. Motivated by this challenge, in this thesis was address the problem of integrating and querying multiple heterogeneous Linked Data sets through ontological rules. Firstly, we propose a formalisation of the notion of a peer-to-peer Linked Data integration system, where the mappings between peers comprise schema-level mappings and equality constraints between different IRIs; we call this formalism an RDF Peer System(RPS). We show that the semantics of the mappings preserve tractability of answering Basic Graph Pattern (BGP) SPARQL queries against the data stored in the RDF sources and the set of constraints given by the RPS mappings. Then, we address the problem of SPARQL query rewriting under RPSs and we show that it is not possible to rewrite an input BGP SPARQL query into a SPARQL 1.0 query under general RPSs, as the RPS peer mappings are not first-order-rewritable rules; this is a major drawback of general RPSs since data materialisation is required to exploit their full semantics. With the adoption of the more recent standard SPARQL 1.1 and its property paths we are able to extend the expressivity of the target language beyond first-order by including regular expressions in the body of the target SPARQL queries, that is, by expressing conjunctive two-way regular path queries (C2RPQs). Following this idea, in the second part of the thesis we step away from the language of RPSs to conduct a study on C2RPQ-rewritability under a broader ontology language. We define [ELHI`inh] (harmless linear ELHI), an ontology language that generalises both the DL-Lite[R] and linear ELH description logics. We prove the rewritability of instance queries (queries with a single atom in their body) under [ELHI`inh] knowledge bases with C2RPQs as the target language, presenting a query rewriting algorithm that makes use of non-deterministic finite-state automata. Following from that, we propose a query rewriting algorithm for answering conjunctive queries under [ELHI`inh] knowledge bases, with C2RPQs as the target language. Since C2RPQs can be straightforwardly expressed in SPARQL 1.1 by means of property paths, we believe that our approach is directly applicable to real-world querying settings. Lastly, we undertake a complexity analysis for query answering under [ELHI`inh]. We analyse the computational cost of query answering in terms of both data complexity (where the ontology and the query are fixed and the data alone is a variable input)and combined complexity (where query, ontology and data all constitute the variable input). We show that answering instance queries under [ELHI`inh] is NLogSpace-complete for data complexity and in PTime for combined complexity; we also show that answering CQs under [ELHI`inh] is NLogSpace-complete for data complexity and NP-complete for combined complexity

Towards Personalized and Human-in-the-Loop Document Summarization

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.Comment: PhD thesi

Knowledge-driven entity recognition and disambiguation in biomedical text

Entity recognition and disambiguation (ERD) for the biomedical domain are notoriously difficult problems due to the variety of entities and their often long names in many variations. Existing works focus heavily on the molecular level in two ways. First, they target scientific literature as the input text genre. Second, they target single, highly specialized entity types such as chemicals, genes, and proteins. However, a wealth of biomedical information is also buried in the vast universe of Web content. In order to fully utilize all the information available, there is a need to tap into Web content as an additional input. Moreover, there is a need to cater for other entity types such as symptoms and risk factors since Web content focuses on consumer health. The goal of this thesis is to investigate ERD methods that are applicable to all entity types in scientific literature as well as Web content. In addition, we focus on under-explored aspects of the biomedical ERD problems -- scalability, long noun phrases, and out-of-knowledge base (OOKB) entities. This thesis makes four main contributions, all of which leverage knowledge in UMLS (Unified Medical Language System), the largest and most authoritative knowledge base (KB) of the biomedical domain. The first contribution is a fast dictionary lookup method for entity recognition that maximizes throughput while balancing the loss of precision and recall. The second contribution is a semantic type classification method targeting common words in long noun phrases. We develop a custom set of semantic types to capture word usages; besides biomedical usage, these types also cope with non-biomedical usage and the case of generic, non-informative usage. The third contribution is a fast heuristics method for entity disambiguation in MEDLINE abstracts, again maximizing throughput but this time maintaining accuracy. The fourth contribution is a corpus-driven entity disambiguation method that addresses OOKB entities. The method first captures the entities expressed in a corpus as latent representations that comprise in-KB and OOKB entities alike before performing entity disambiguation.Die Erkennung und Disambiguierung von Entitäten für den biomedizinischen Bereich stellen, wegen der vielfältigen Arten von biomedizinischen Entitäten sowie deren oft langen und variantenreichen Namen, große Herausforderungen dar. Vorhergehende Arbeiten konzentrieren sich in zweierlei Hinsicht fast ausschließlich auf molekulare Entitäten. Erstens fokussieren sie sich auf wissenschaftliche Publikationen als Genre der Eingabetexte. Zweitens fokussieren sie sich auf einzelne, sehr spezialisierte Entitätstypen wie Chemikalien, Gene und Proteine. Allerdings bietet das Internet neben diesen Quellen eine Vielzahl an Inhalten biomedizinischen Wissens, das vernachlässigt wird. Um alle verfügbaren Informationen auszunutzen besteht der Bedarf weitere Internet-Inhalte als zusätzliche Quellen zu erschließen. Außerdem ist es auch erforderlich andere Entitätstypen wie Symptome und Risikofaktoren in Betracht zu ziehen, da diese für zahlreiche Inhalte im Internet, wie zum Beispiel Verbraucherinformationen im Gesundheitssektor, relevant sind. Das Ziel dieser Dissertation ist es, Methoden zur Erkennung und Disambiguierung von Entitäten zu erforschen, die alle Entitätstypen in Betracht ziehen und sowohl auf wissenschaftliche Publikationen als auch auf andere Internet-Inhalte anwendbar sind. Darüber hinaus setzen wir Schwerpunkte auf oft vernachlässigte Aspekte der biomedizinischen Erkennung und Disambiguierung von Entitäten, nämlich Skalierbarkeit, lange Nominalphrasen und fehlende Entitäten in einer Wissensbank. In dieser Hinsicht leistet diese Dissertation vier Hauptbeiträge, denen allen das Wissen von UMLS (Unified Medical Language System), der größten und wichtigsten Wissensbank im biomedizinischen Bereich, zu Grunde liegt. Der erste Beitrag ist eine schnelle Methode zur Erkennung von Entitäten mittels Lexikonabgleich, welche den Durchsatz maximiert und gleichzeitig den Verlust in Genauigkeit und Trefferquote (precision and recall) balanciert. Der zweite Beitrag ist eine Methode zur Klassifizierung der semantischen Typen von Nomen, die sich auf gebräuchliche Nomen von langen Nominalphrasen richtet und auf einer selbstentwickelten Sammlung von semantischen Typen beruht, die die Verwendung der Nomen erfasst. Neben biomedizinischen können diese Typen auch nicht-biomedizinische und allgemeine, informationsarme Verwendungen behandeln. Der dritte Beitrag ist eine schnelle Heuristikmethode zur Disambiguierung von Entitäten in MEDLINE Kurzfassungen, welche den Durchsatz maximiert, aber auch die Genauigkeit erhält. Der vierte Beitrag ist eine korpusgetriebene Methode zur Disambiguierung von Entitäten, die speziell fehlende Entitäten in einer Wissensbank behandelt. Die Methode wandelt erst die Entitäten, die in einem Textkorpus ausgedrückt aber nicht notwendigerweise in einer Wissensbank sind, in latente Darstellungen um und führt anschließend die Disambiguierung durch

Building information modeling – A game changer for interoperability and a chance for digital preservation of architectural data?

Digital data associated with the architectural design-andconstruction process is an essential resource alongside -and even past- the lifecycle of the construction object it describes. Despite this, digital architectural data remains to be largely neglected in digital preservation research – and vice versa, digital preservation is so far neglected in the design-and-construction process. In the last 5 years, Building Information Modeling (BIM) has seen a growing adoption in the architecture and construction domains, marking a large step towards much needed interoperability. The open standard IFC (Industry Foundation Classes) is one way in which data is exchanged in BIM processes. This paper presents a first digital preservation based look at BIM processes, highlighting the history and adoption of the methods as well as the open file format standard IFC (Industry Foundation Classes) as one way to store and preserve BIM data

Digital Classical Philology

The buzzwords “Information Society” and “Age of Access” suggest that information is now universally accessible without any form of hindrance. Indeed, the German constitution calls for all citizens to have open access to information. Yet in reality, there are multifarious hurdles to information access – whether physical, economic, intellectual, linguistic, political, or technical. Thus, while new methods and practices for making information accessible arise on a daily basis, we are nevertheless confronted by limitations to information access in various domains. This new book series assembles academics and professionals in various fields in order to illuminate the various dimensions of information's inaccessability. While the series discusses principles and techniques for transcending the hurdles to information access, it also addresses necessary boundaries to accessability.This book describes the state of the art of digital philology with a focus on ancient Greek and Latin. It addresses problems such as accessibility of information about Greek and Latin sources, data entry, collection and analysis of Classical texts and describes the fundamental role of libraries in building digital catalogs and developing machine-readable citation systems