Personal Knowledge Graphs: Use Cases in e-learning Platforms

TL;DR

This paper explores the development and application of Personal Knowledge Graphs in e-learning platforms to enhance personalization, explainability, and data interlinking, inspired by their success in the medical domain.

Contribution

It introduces a novel PKG development method using ontology and linked data, tailored for e-learning, with preliminary positive results in personalization and data management.

Findings

Improved personalized recommendations in e-learning platforms.

Enhanced user and group-specific data representation.

Potential for increased explainability and privacy in user data.

Abstract

Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users' featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.