Embedding knowledge and value of a brand into sustainability for differentiation

This is the post-print version of the final paper published in the Journal of World Business (under the provisional title "Embedding sustainability into brand knowledge and brand value for brand differentiation"). The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.Organisations offer products to consumers, buyers often question if the product or its production process are linked to the environmental, social or economic challenges being faced by mankind. Inquisitiveness of customers in this direction points towards an opportunity for marketers to create differentiation based on the concerns of brand towards overall issue of sustainability. The authors have synthesized knowledge from various domains with a positivistic approach to understand sustainability from the perspective of branding. Using empirical knowledge this study recommends embedding sustainability into brand knowledge and brand value for creating a differentiation for the brand in a competitive market

Ensemble-Based Fact Classification with Knowledge Graph Embeddings

Numerous prior works have shown how we can use Knowledge Graph Embeddings (KGEs) for ranking unseen facts that are likely to be true. Much less attention has been given on how to use KGEs for fact classification, i.e., mark unseen facts either as true or false. In this paper, we tackle this problem with a new technique that exploits ensemble learning and weak supervision, following the principle that multiple weak classifiers can make a strong one. Our method is implemented in a new system called DuEL. DuEL post-processes the ranked lists produced by the embedding models with multiple classifiers, which include supervised models like LSTMs, MLPs, and CNNs and unsupervised ones that consider subgraphs and reachability in the graph. The output of these classifiers is aggregated using a weakly supervised method that does not need ground truths, which would be expensive to obtain. Our experiments show that DuEL produces a more accurate classification than other existing methods, with improvements up to 72% in terms of F1 score. This suggests that weakly supervised ensemble learning is a promising technique to perform fact classification with KGEs

DCWEB-SOBA: Deep Contextual Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification

In this paper, we propose the use of deep contextualised word embeddings to semi-automatically build a domain sentiment ontology. Compared to previous research, we use deep contextualised word embeddings to better cope with various meanings of words. A state-of-the-art hybrid method is used for aspect-based sentiment analysis, called HAABSA++, to evaluate our obtained ontology on the SemEval-2016 restaurant dataset. We achieve a prediction accuracy of 81.85% for the hybrid model with our ontology, which outperforms the hybrid model with other considered ontologies. Furthermore, we find that the ontology obtained from our proposed domain sentiment ontology builder, called DCWEB-SOBA, on itself improves the accuracy for the conclusive cases from 83.04% to 84.52% compared to the ontology builder based on non-contextual word embeddings, WEB-SOBA