Improving Effectiveness of Knowledge Graph Generation Rule Creation and Execution

@phdthesis{heyvaert_phd_2019,
    title = {Improving Effectiveness of Knowledge Graph Generation Rule Creation and Execution},
    author = {Heyvaert, Pieter},
    year = 2019,
    month = oct,
    school = {Ghent University},
    address = {Ghent, Belgium},
    url = {https://pieterheyvaert.com/phd/dissertation},
    pdf = {https://pieterheyvaert.com/phd/dissertation/dissertation.pdf},

    abstract = {More and more data is generated by an increasing number of agents (applications and devices)
    that are connected to the Internet,
    all contributing to data that is available on the Web.
    When this data is analyzed, combined, and shared
    powerful, new techniques can be designed and deployed,
    such as artificial intelligence applied by personal assistants
    (e.g., Apple's Siri and Amazon Alexa),
    improved search engines
    (e.g., Google Search and Microsoft's Bing), and
    decentralized data storages (as opposed to a large, central storage used by big companies).
    Due to this increase in data,
    methods originally used to
    model data have become insufficient:
    they isolate the data,
    which resides in different data sources, and
    make it hard for agents to exchange data on the Web.
    Data cannot be easily exchanged between agents,
    because every agent uses a different data model to
    describe the concepts and relationships of the data they generate and use.
    The Semantic Web offers a solution to this problem:
    knowledge graphs,
    which are directed graph that
    provides semantic descriptions of entities and their relationships.
    A common way to generate these knowledge graphs is by using rules.
    During my PhD I investigated how we can improve
    the creation of these rules,
    the refinement of these rules, and
    the execution of these rules.},
}