@inproceedings{c125572270af4b27b01c9b2569df4eb3,
title = "A Framework for Automatic Personalised Ontology Learning",
abstract = "Understanding or acquiring a user's information needs from their local information repository (e.g. a set of example-documents that are relevant to user information needs) is important in many applications. However, acquiring the user's information needs from the local information repository is very challenging. Personalised ontology is emerging as a powerful tool to acquire the information needs of users. However, its manual or semi-Automatic construction is expensive and time-consuming. To address this problem, this paper proposes a model to automatically learn personalised ontology by labelling topic models with concepts, where the topic models are discovered from a user's local information repository. The proposed model is evaluated by comparing against ten baseline models on the standard dataset RCV1 and a large ontology LCSH. The results show that the model is effective and its performance is significantly improved.",
keywords = "Labelling Topic Models, Ontology Mining, Personalisation, User Information Needs, Web Intelligence",
author = "Bashar, {Md Abul} and Yuefeng Li and Yang Gao",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016 ; Conference date: 13-10-2016 Through 16-10-2016",
year = "2017",
month = jan,
day = "12",
doi = "10.1109/WI.2016.0025",
language = "English",
series = "Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "105--112",
booktitle = "Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016",
address = "United States",
}