Personal Knowledge Graphs: Use Cases in e-learning Platforms

Ilkou, Eleni

doi:10.1145/3487553.3524196

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…For example, in the medical domain, a PKG can be used to represent a patient via a domain-specific KG that contains diseases and symptoms. Also, PKGs have been suggested recently in the educational domain for e-learning platforms as an intelligent alternative to users' profiles with the aim to achieve higher personalisation, collaboration and semantic recommendations [45]. In addition to this work, our CollabGraph builds on top of PKGs to offer a practical solution in a collaborative e-learning platform.…”

Section: E Personal Information Managementmentioning

confidence: 99%

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CollabGraph: A Graph-Based Collaborative Search Summary Visualization

Ilkou

Tolmachova

Fisichella

et al. 2023

IEEE Trans. Learning Technol.

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Currently, the search history in search engines is presented in a list view of some combination of enumerated results by title, url or search query. However, this classical list view is not ideal in collaborative search environments as it does not always assist users in understanding collaborators' search history results and the project's status. We present CollabGraph, a system for graph-based summary visualisation in collaborative search learning environments. Our system differentiates from existing solutions by visualising the summary of the collaboration results in a graph and having its core Personal Knowledge Graphs (PKGs) for each user. Our research questions concentrate around the CollabGraph's usefulness, preference, and enhancement of participation of student's and teacher's feedback compared to the list view of search history results. We evaluate our approach with an online questionnaire in 6 different project-based Search as Learning (SaL) scenarios. The evaluation of users' experience indicates that the CollabGraph is useful, highly likeable and could benefit users' participation and teacher's feedback by providing more precise insights into the project status. Our approach helps users better perceive about everyone's work, and it is a highly preferable feature alongside the list view. Also, the results demonstrate that graph summary visualisations, such as the CollabGraph, are more suitable for closed-end scenarios and collaborative projects with many participants.

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Section: E Personal Information Managementmentioning

confidence: 99%

“…Another development part involves the exploration of PKGs. The latter is a new area in the literature, and we are joining this wave at its early steps [45]. Ontologies in education and user modeling can assist in personalisation and be an addition to PKGs [73].…”

Section: Future Workmentioning

confidence: 99%

CollabGraph: A Graph-Based Collaborative Search Summary Visualization

Ilkou

Tolmachova

Fisichella

et al. 2023

IEEE Trans. Learning Technol.

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“…The post-COVID-19 pandemic-stricken world has seen a boom in the use of e-learning platforms as not just an alternative but at times equivalent to physical classroom learning. A PKG can enhance the learning experience on such platforms (Ilkou, 2022). By taking into account the data generated by users on e-learning platforms, the PKG can help predict the learning path of the user, all the while taking into account the various accessibility options and the academic and psychological profile of the user.…”

Section: Education and Researchmentioning

confidence: 99%

“…It can also help in recommending courses that are better suited to a particular user's needs and capacity, by availing the resources of external knowledge bases like eDoers. 5 The idea of using a PKG to enhance the learning experience can be extended to a group or collaborative setting by taking into account collaborative search or the Searching as Learning technique (Ilkou, 2022).…”

Section: Education and Researchmentioning

confidence: 99%

A comprehensive survey of personal knowledge graphs

Chakraborty

Sanyal

2023

WIREs Data Min & Knowl

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Information that can encapsulate a person's daily life and its different aspects provides insightful knowledge. This knowledge can prove to be more useful than general knowledge for improving personalized tasks. When it comes to storing such knowledge, personal knowledge graphs (PKGs) come in as handy saviors. PKGs are knowledge graphs which store details that are pertinent to a user but not, in general, useful to the rest of humanity. Conversational agents can access these PKGs to answer queries related to the user's day‐to‐day life, whereas recommender systems can harness the knowledge stored in PKGs to make personalized suggestions. Despite the immense applicability of PKGs, there has not been significant research in this area. We present an extensive review of PKGs. We categorize them according to the domains in which they are most relevant; in particular, we highlight the use of PKGs in medicine, finance, and education and research. We also categorize the different ways of constructing a PKG based on the source of data required for such constructions. Furthermore, we discuss the limitations of PKGs and suggest directions for future work.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Artificial Intelligence

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“…Maria Becker suggests this framework could be used for enriching texts and even enriching knowledge bases themselves, she also uses CO-NNECT to add knowledge path constraints to language models with an improvement in sentence generation representation of implicit knowledge (Becker et al, 2021c) Dessì et al (2021 Uses State-of-the-art NLP Tools and resources to create a Scientific Knowledge Graph representing knowledge contained inside academic productions in a natural language format. Ilkou (2022) uses Entity Extraction and the DBpedia Knowledge graph to generate Personal Knowledge Graphs with specific e-learning users' personal information regarding learning profiles and activities, looking to enhance the learning experience.…”

Section: Similar Work In Literaturementioning

confidence: 99%

On Automatic Generation of Knowledge Connections

FRAGA¹

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I would like to thank my girlfriend, Maria Clara, for supporting me, pushing me, loving me, and helping me in several ways during this process.I would like to thank my advisor, who guided me through important decisions, gave me the freedom to trail my own path, and was very important to bring this work to a conclusion.I would like to thank my friends, who inspire me, support me, cherish me and make my life better.I am extremely grateful to the PKM Community on Twitter, for inspiring me, showing me important resources, and providing me with ideas and pathways for making this work come to fruition.Within this community, I would like to especially thank Tiago Forte and RJ Nestor for teaching me about workflows and mentalities of digital productivity in Personal Knowledge Management.This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001.

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Personal Knowledge Graphs: Use Cases in e-learning Platforms

Cited by 10 publications

References 12 publications

CollabGraph: A Graph-Based Collaborative Search Summary Visualization

CollabGraph: A Graph-Based Collaborative Search Summary Visualization

A comprehensive survey of personal knowledge graphs

On Automatic Generation of Knowledge Connections

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