With this
work, we introduce a novel method for the unsupervised learning of conceptual
hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such
distinguishing itself from the majority of research literature on the topic
which is primarily focused on building ontologies from a vast array of
different types of data sources, both structured and unstructured, to support
various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee.
We first elaborate on mutually informing disciplines of philosophy and computer
science, or more specifically the relationship between
metaphysics, epistemology, ontology, computing and AI, followed by a
technically in-depth discussion of DEBRA, our dependency
tree based concept hierarchy constructor, which as its name alludes to,
constructs a conceptual map in the form of a directed graph which illustrates
the concepts, their respective relations, and the implied ontological structure
of the concepts as encoded in the text, decoded with standard Python NLP
libraries such as spaCy and NLTK. With this work we hope to both augment the
Knowledge Representation literature with opportunities for intellectual
advancement in AI with more intuitive, less analytical, and
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