@inproceedings{ee95183e5a8441da9ae0cbf0dc064ef4,
title = "Event identification and assertion from social media using auto-extendable knowledge base",
abstract = "Social media have become an important source of data and can provide near-instantaneous information which can be analysed to generate predictive models and to support decision making. Much work has been done in short message analysis such as trend analysis, short message classification, etc. However, to generate an accurate and concise conclusion/assertion from all the relevant information remains challenging. In this paper we propose a method to analyse microblog messages at both 'word/term' level and 'concept' level to generate assertions accurately and instantly. To analyse the concept level, we define a small seed ontology which is a semi-automatically generated extension of an existing ontology. By doing this we achieve both accurate assertions and avoid the costly overhead of defining the whole knowledgebase manually. We then use the proposed method to make traffic assertions from a microblog stream to demonstrate the advantages of the approach.",
keywords = "Event detection, Event identification, Microblog analysis, Ontology learning, Social media analysis, Tweet analysis",
author = "Suliman, {Ahmed Talal} and {Al Kaabi}, Khaled and Di Wang and Ahmad Al-Rubaie and {Al Dhanhani}, Ahmed and Dymitr Ruta and John Davies and Clarke, {Sandra Stincic}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = oct,
day = "31",
doi = "10.1109/IJCNN.2016.7727781",
language = "British English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4443--4450",
booktitle = "2016 International Joint Conference on Neural Networks, IJCNN 2016",
address = "United States",
}