A Heuristically Modified FP-Tree for Ontology Learning with Applications in Education

TL;DR

This paper introduces a heuristically modified FP-Tree for ontology learning from unstructured text, utilizing DFA for concept extraction and rule-based pattern mining, which improves question-answering accuracy in educational contexts.

Contribution

It presents a novel heuristic approach combining DFA-based concept extraction and pattern mining for ontology learning, enhancing question-answering systems in education.

Findings

80% question answering success with our ontology

Compared to Text2Onto, our method significantly improves coverage

Latent Semantic Analysis aligns best with expert ratings

Abstract

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.