Ontology-based Adaptive e-Textbook Platform for Student and Machine Co-Learning

TL;DR

This paper proposes an ontology-based adaptive e-textbook platform that integrates knowledge graphs and intelligent information retrieval to enhance interactivity and learning effectiveness in electronic textbooks.

Contribution

It introduces a novel method for constructing knowledge graphs for e-books with minimal overhead and applies typed similarity queries for improved information retrieval.

Findings

Knowledge graph construction is efficient with low overhead.

Typed similarity queries enable effective information retrieval.

Case study shows promising application in e-textbook enhancement.

Abstract

The use of electronic textbooks (e-book) has been heavily studied over the years due to their flexibility, accessibility, interactivity and extensibility. Yet current shortcomings of e-book, which is often just a digitized version of the original book, does not encourage adoption. Consequently, this leads to a rethinking of e-book that should incorporate current technologies to augment its capabilities, where inclusion of information search and organization tools have shown to be favorable. This paper is on a preliminary work to add intelligence into such tools in terms of information retrieval. Construction of knowledge graph for e-book material with little overhead is first introduced. Information retrieval through typed similarity query is then performed via random walk. Case study demonstrate the applicability of the e-book platform, with promising application and advancement in the area of electronic textbooks.