Confusion Matrix

The performance metrics used to compare classification performance are typically represented using elements in the confusion matrix, which is generated by the machine learning model on a test sample. Figure 1 denotes the template of a confusion matrix for a two-class classification problem, where the class of an instance is either positive or negative (i.e.,Continue reading “Confusion Matrix”

Introduction to DBpedia

Semantic Web Semantic Web enables machines to browse the knowledge distributed across the Web. Consider a Wikipedia page that provides knowledge to human readers. The knowledge contained in the Wikipedia page is opaque from the perspective of the machines, as they “see” nothing but a presentation markup of the Wikipedia page. The idea of SemanticContinue reading “Introduction to DBpedia”

Why Literature-Based Discovery?

The scientific literature is growing at an unprecedented rate and it is estimated that the global scientific output doubles every nine years. To date, scientific digital libraries consist of millions of research publications, with thousands of these being added every day. For instance, consider MEDLINE (https://www.nlm.nih.gov/bsd/medline.html), a popular bibliographical database. It contains more than 26Continue reading “Why Literature-Based Discovery?”