Yes / The domain of cultural variations in interpersonal communication is becoming increasingly
important in various areas, including human-human interaction (e.g. business settings) and humancomputer
interaction (e.g. during simulations, or with social robots). User generated content (UGC)
in social media can provide an invaluable source of culturally diverse viewpoints for supporting the
understanding of cultural variations. However, discovering and organizing UGC is notoriously
challenging and laborious for humans, especially in ill-defined domains such as culture. This calls for
computational approaches to automate the UGC sensemaking process by using tagging, linking and
exploring. Semantic technologies allow automated structuring and qualitative analysis of UGC, but
are dependent on the availability of an ontology representing the main concepts in a specific
domain. For the domain of cultural variations in interpersonal communication, no ontological model
exists. This paper presents the first such ontological model, called AMOn+, which defines cultural
variations and enables tagging culture-related mentions in textual content. AMOn+ is designed
based on a novel interdisciplinary approach that combines theoretical models of culture with
crowdsourced knowledge (DBpedia). An evaluation of AMOn+ demonstrated its fitness-for-purpose
regarding domain coverage for annotating culture-related concepts mentioned in text corpora. This
ontology can underpin computational models for making sense of UGC.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/10246 |
| Date | 05 May 2017 |
| Creators | Thakker, Dhaval, Karanasios, S, Blanchard, E., Lau, L., Dimitrova, V. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted Manuscript |
| Rights | © 2016 Wiley. This is the peer reviewed version of the following article: Thakker D, Karanasios S, Blanchard E et al (2016) Ontology for cultural variations in interpersonal communication: building on theoretical models and crowdsourced knowledge. Journal of the Association for Information Science and Technology, which has been published in final form at http://dx.doi.org/ 10.1002/asi.23824. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. |
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