  We propose a heuristically modified FP-Tree for ontology learning from text.
Unlike previous research, for concept extraction, we use a regular expression
parser approach widely adopted in compiler construction, i.e., deterministic
finite automata (DFA). Thus, the concepts are extracted from unstructured
documents. For ontology learning, we use a frequent pattern mining approach and
employ a rule mining heuristic function to enhance its quality. This process
does not rely on predefined lexico-syntactic patterns, thus, it is applicable
for different subjects. We employ the ontology in a question-answering system
for students' content-related questions. For validation, we used textbook
questions/answers and questions from online course forums. Subject experts
rated the quality of the system's answers on a subset of questions and their
ratings were used to identify the most appropriate automatic semantic text
similarity metric to use as a validation metric for all answers. The Latent
Semantic Analysis was identified as the closest to the experts' ratings. We
compared the use of our ontology with the use of Text2Onto for the
question-answering system and found that with our ontology 80% of the questions
were answered, while with Text2Onto only 28.4% were answered, thanks to the
finer grained hierarchy our approach is able to produce.
