The Internet as we know it today has sustained continuous evolution since its creation, radically changing means of communication and ways in which commerce is globally operated. From the World Wide Web to the two-way video calls, it has shifted the ways people communicate and societies function. The Internet itself was first conceived as a network that would enable the communication between multiple trusted and known hosts, but as the time passed, it has notably evolved. Due to the significant adoption of Internet-connected devices (phones, personal computers, tablets...), the initial device homogeneity has shifted towards an extremely heterogeneous environment in which many different devices consume and publish resources, also referred as services. As the number of connected devices and resources increases, it becomes critical to building systems that enable the autonomic publication, consumption, and retrieval of those resources. As the inherent complexity of systems continues to grow, it is essential to set boundaries to their achievable capabilities. The traditional approaches to network-based computing are not sufficient, and new reference approaches should be presented. In this context the Future Internet (FI) term emerges, a worldwide execution environment connecting large sets of heterogeneous and autonomic devices and resources. In such environments, systems leverage service annotations to fulfil emerging goals and dynamically organise resources based on interests. Although research has been conducted in those areas, active research is being carried out in the following areas: extensible machine-readable annotation of services, dynamic service discovery, architectural approaches for decentralised systems, and interest-focused dynamic service organisations. These concepts will be explained in the next section, as they will serve to contextualise the later presented problem statement and research questions.

The advent of the era of big data on the Web has made automatic web information extraction an essential tool in data acquisition processes. Unfortunately, automated solutions are in most cases more error prone than those created by humans, resulting in dirty and erroneous data. Automatic repair and cleaning of the extracted data is thus a necessary complement to information extraction on the Web. This thesis investigates the problem of inducing cleaning rules on web extracted data in order to (i) repair and align the data w.r.t. an original target schema, (ii) produce repairs that are as generic as possible such that different instances can benefit from them. The problem is addressed from three different angles: replace cross-site redundancy with an ensemble of entity recognisers; produce general repairs that can be encoded in the extraction process; and exploit entity-wide relations to infer common knowledge on extracted data. First, we present ROSeAnn, an unsupervised approach to integrate semantic annotators and produce a unied and consistent annotation layer on top of them. Both the diversity in vocabulary and widely varying accuracy justify the need for middleware that reconciles different annotator opinions. Considering annotators as "black-boxes" that do not require per-domain supervision allows us to recognise semantically related content in web extracted data in a scalable way. Second, we show in WADaR how annotators can be used to discover rules to repair web extracted data. We study the problem of computing joint repairs for web data extraction programs and their extracted data, providing an approximate solution that requires no per-source supervision and proves effective across a wide variety of domains and sources. The proposed solution is effective not only in repairing the extracted data, but also in encoding such repairs in the original extraction process. Third, we investigate how relationships among entities can be exploited to discover inconsistencies and additional information. We present RuDiK, a disk-based scalable solution to discover first-order logic rules over RDF knowledge bases built from web sources. We present an approach that does not limit its search space to rules that rely on "positive" relationships between entities, as in the case with traditional mining of constraints. On the contrary, it extends the search space to also discover negative rules, i.e., patterns that lead to contradictions in the data.

Electronic Health Record (EHR) creates a huge number of databases which are being updated dynamically. Major goal of interoperability in healthcare is to facilitate the seamless exchange of healthcare related data and an environment to supports interoperability and secure transfer of data. The health care organisations face difficulties in exchanging patient’s health care information and laboratory reports etc. due to a lack of semantic interoperability. Hence, there is a need of semantic web technologies for addressing healthcare interoperability problems by enabling various healthcare standards from various healthcare entities (doctors, clinics, hospitals etc.) to exchange data and its semantics which can be understood by both machines and humans. Thus, a framework with a similarity analyser has been proposed in the thesis that dealt with semantic interoperability. While dealing with semantic interoperability, another consideration was the use of word embedding and ontology for knowledge discovery. In medical domain, the main challenge for medical information extraction system is to find the required information by considering explicit and implicit clinical context with high degree of precision and accuracy. For semantic similarity of medical text at different levels (conceptual, sentence and document level), different methods and techniques have been widely presented, but I made sure that the semantic content of a text that is presented includes the correct meaning of words and sentences. A comparative analysis of approaches included ontology followed by word embedding or vice-versa have been applied to explore the methodology to define which approach gives better results for gaining higher semantic similarity. Selecting the Kidney Cancer dataset as a use case, I concluded that both approaches work better in different circumstances. However, the approach in which ontology is followed by word embedding to enrich data first has shown better results. Apart from enriching the EHR, extracting relevant information is also challenging. To solve this challenge, the concept of analogy has been applied to explain similarities between two different contents as analogies play a significant role in understanding new concepts. The concept of analogy helps healthcare professionals to communicate with patients effectively and help them understand their disease and treatment. So, I utilised analogies in this thesis to support the extraction of relevant information from the medical text. Since accessing EHR has been challenging, tweets text is used as an alternative for EHR as social media has appeared as a relevant data source in recent years. An algorithm has been proposed to analyse medical tweets based on analogous words. The results have been used to validate the proposed methods. Two experts from medical domain have given their views on the proposed methods in comparison with the similar method named as SemDeep. The quantitative and qualitative results have shown that the proposed analogy-based method bring diversity and are helpful in analysing the specific disease or in text classification.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

This thesis deals with intelligent systems for support of the semantic annotation of text. It discusses the motivation for creation of such systems and state of the art in the areas of their usage. The thesis also describes newly proposed and realised annotation system which realizes advanced functions of semantic filtering and presentation of annotation suggestion alternatives in a unique way. The results of finished experiments clearly show the advantages of proposed solution. They also prove that the user interface of the annotation tools affects the annotation process. The optimisation of displayed information for the task of disambiguation of ambiguous entity names was done and proposed methods to speedup and increase of quality of the created annotations was experimentally evaluated. The comparison with the Protégé general tool has proven the benefits of created system for collaborative ontology creation which should be anchored in the text. In the conclusion, all achieved results are analysed and summarized.