Ontology for cultural variations in interpersonal communication: Building on theoretical models and crowdsourced knowledge. (5th May 2017)

• Record Type: Journal Article
• Authors: Thakker, Dhavalkumar; Karanasios, Stan; Blanchard, Emmanuel; Lau, Lydia; Dimitrova, Vania
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 68:Number 6(2017:Jun.)
• Page Start: 1411
• Page End: 1428
• Publication Date: 2017-05-05
• Subjects: Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.23824
• Languages: English
• ISSNs: 2330-1635

Abstract

Abstract : The domain of cultural variations in interpersonal communication is becoming increasingly important in various areas, including human–human interaction (e.g., business settings) and human–computer 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.