Ontology-based information extraction and reservoir computing for topic detection from blogosphere's content : a case study about BBC backstage

Arguello-Casteleiro, M and Fernandez-Prieto, MJ 2014, 'Ontology-based information extraction and reservoir computing for topic detection from blogosphere's content : a case study about BBC backstage' , in: Research and Development in Intelligent Systems XXXI : Incorporating Applications and Innovations in Intelligent Systems XXII , Research and Development in Intelligent Systems, XIV , Springer International Publishing, Switzerland, pp. 333-338.

[img] PDF - Published Version
Restricted to Repository staff only

Download (602kB) | Request a copy

Abstract

This research study aims at detecting topics and extracting themes(subtopics) from the blogosphere’s content while bridging the gap between the Social Web and the Semantic Web. The goal is to detect certain types of information from collections of blogs’ and microblogs’ narratives that lack explicit semantics. The approach presented introduces a novel approach that blends together two young paradigms: Ontology-Based Information Extraction (OBIE) and Reservoir Computing (RC). The novelty of the work lies in integrating ontologies and RC as well as the pioneering use of RC with social media data. Experiments with retrospect data from blogs and Twitter microblogs provide valuable insights into the BBC Backstage initiative and prove the viability of the approach presented in terms of scalability, computational complexity,and performance.

Item Type: Book Section
Editors: Bramer, M and Petridis, M
Themes: Media, Digital Technology and the Creative Economy
Schools: Schools > School of Humanities, Languages & Social Sciences > Centre for Linguistics and Applied Linguistics
Publisher: Springer International Publishing
Refereed: Yes
Series Name: Research and Development in Intelligent Systems
ISBN: 9783319120690
Related URLs:
Funders: Non funded research
Depositing User: MJ Fernandez-Prieto
Date Deposited: 17 Jun 2015 16:53
Last Modified: 16 Feb 2022 16:47
URI: https://usir.salford.ac.uk/id/eprint/35287

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year