Browsing by Author "Baker, Christopher J.O."

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    Generating SADI semantic web services from declarative descriptions
    (University of New Brunswick, 2019) Al Manir, Mohammad Sadnan; Baker, Christopher J.O.; Boley, Harold
    Accessing information stored in databases remains a challenge for many types of end users. In contrast, accessing information from knowledge bases allows for more intuitive query formulation techniques. Whereas knowledge bases can be directly instantiated by the materialization of data according to a reference semantic model, a more scalable approach is to rely on queries formulated using ontologies rewritten as database queries at query time. Both of these approaches allow semantic querying, which involves the application of domain knowledge written in the form of axioms and declarative semantic mapping rules. In neither case are users required to interact with the underlying database schemas. A further approach offering semantic querying relies on SADI Semantic Web services to access relational databases. In this approach, services brokering access to specific data sets can be automatically discovered, orchestrated into workflows, and invoked to execute queries performing data retrieval or data transformation. This can be achieved using specialized query clients built for interfacing with services. Although this approach provides a successful way of accessing data, creating services requires advanced skills in modeling RDF data and domain ontologies, writing of program code and SQL queries. In this thesis we propose the Valet SADI framework as a solution for automation of SADI Semantic Web service creation. Valet SADI represents a novel architecture comprising four modules which work together to generate and populate services into queryable registries. In the first module declarative semantic mappings are written between source databases and domain ontologies. In a second module, the inputs and outputs of a service are defined in a service ontology with reference to domain ontologies. The third module creates un-instantiated SQL queries automatically based on a semantic query, the target database, domain ontologies and mapping rules. The fourth module produces the source code for a complete and functional SADI service containing the SQL query. The inputs to the first two modules are verified manually while the other modules are fully automated. Valet SADI is demonstrated in two use cases, namely, the creation of a queryable registry of services for surveillance of hospital acquired infections, and the preservation of interoperability in a malaria surveillance infrastructure.
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    Semantic enrichment and similarity approximation for biomedical sequence images
    (University of New Brunswick, 2017) Bukhari, Syed Ahmad Chan; Baker, Christopher J.O.
    Scientific publications are considered as the most up-to-date resource of ongoing research activities and scientific knowledge. Efficient practices for accessing biomedical publications are key to allowing a timely transfer of information from the scientific research community to peer investigators and other healthcare practitioners. Biomedical sequence images published within the literature play a central role in life science discoveries. Whereas advanced text-mining pipelines for information retrieval and knowledge extraction are now commonplace methodologies for processing documents, the ongoing challenges associated with knowledge management and utility operations unique to biomedical image data are only recently gaining recognition. Sequence images depicting key findings of research papers contain rich information derived from a wide range of biomedical experiments. Searching for relevant sequence images is however error prone as images are still opaque to information retrieval and knowledge extraction engines. Specifically, there is no explicit description or annotation of the sequence image content. Moreover, traditional biomedical search engines, which search image captions for relevant keywords only, offer syntactic search mechanisms without regard for the exact meaning of the query. As proposed in this thesis, semantic enrichment of biomedical sequence images is a solution which adopts a combination of technologies to harness the comprehensive information associated with, and contained in, biomedical sequence images. Extracted information from sequence images is used as seed data to aggregate and harvest new annotations from heterogeneous online biomedical resources. Comprehensive semantic enrichment of biomedical images incorporates a variety of knowledge infrastructure components and services including image feature extraction, semantic web data services, linked open data and crowd annotation. Together, these resources make it possible to automatically and/or semi-automatically discover and semantically interlink new information in a way that supports semantic search for sequence images. The resulting enriched sequence images are readily reusable based on their semantic annotations and can be made available for use in ad-hoc data integration activities. Furthermore, to support image reuse this thesis introduces a mechanism for identifying similar sequence images based on fuzzy inference and cosine similarity techniques that can retrieve and classify the related sequence images based on their semantic annotations. The outcomes of this research work will be relevant to a variety of user groups ranging from clinicians and researchers searching with sequence image data.
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    The semantics of persuasion: a case study using phishing emails
    (University of New Brunswick, 2021) van der Laan, Jacob Jan; Lashkari, Arash Habibi; Baker, Christopher J.O.
    As of 2021, phishing emails continue to be the primary means by which network breaches are facilitated. Notwithstanding the development of many tools to detect and block incoming phishing emails, many users continue to be plagued by them on a daily basis. In addition, the nature of phishing emails is changing as the incidence of more personalized forms, such as spear phishing and whaling, prove their effectiveness. These newer forms of phishing are harder to detect using traditional methods and emphasize the need for approaches which seek to enable detection based on persuasion based language features unique to phishing emails. To that end, this thesis draws insights from the phishing process, the applicable behavioural psychology research on persuasion, as well as linguistics, to inform an understanding of how phishing emails persuade. It then proposes a methodology for feature engineering of persuasion language related features for the phishing email domain, based on these insights. A proof of concept model is developed using persuasion based language features, and then implemented and tested using several machine learning algorithms. The performance of this model is as good, if not slightly better, than other more complex and labour intensive efforts which sought to capture semantic meaning using fewer detection features. The thesis concludes with a discussion of potential future work.