… to knowledge engineering as, eg, verification of a knowledge … mechanisms, reduction of a
knowledge base, propagation of … mentioned applications to knowledge engineering, the fuzzy …

… In this study, data discovery has been made by applying data science processes, and the
HCV has been estimated with machine learning methods. By analyzing and visualizing the …

… EDdA’s presentation of ordered knowledge nevertheless has gaps that hinder our ability to
easily use it as machine-readable data. First, there are a more than 10k unclassified articles. …

… rules that guide a logical data modeling process with respect to the specific features of
different NoSQL data models, as well as the conceptual data model and data access patterns, (3) …

… We review the body of knowledge related to ERP failure factors in the form of a systematic
literature mapping (SLM). We pose four sets of research questions and systematically develop …

… ) is utilized that helps for training the data regarding the implicit aspects, and further, helps
to differentiate properly for the testing data with exact polarity score. Optimal feature selection …

… to enhance the knowledge base and deliver value for the application domain. To accomplish
this, we labeled supplemental data in addition to the RDDS data, as described below. …

… This paper gives an overview of the development of the field of Knowledge Engineering …
which considerably shaped research in Knowledge Engineering: Role-limiting Methods and …

… With regard to public health and preventive strategies, this study suggests that big data are
a novel source of data that are largely underused in health, but that can largely inform public …

… changes and producing a large amount of data. Organizations realize that there are so …
huge data volume, and they are urgently turning to explore the ubiquitous assets in their data by …