An exploratory study on utilising the web of linked data for product data mining

The Linked Open Data practice has led to a significant growth of structured data on the Web. While this has created an unprecedented opportunity for research in the field of Natural Language Processing, there is a lack of systematic studies on how such data can be used to support downstream NLP tasks. This work focuses on the e-commerce domain and explores how we can use such structured data to create language resources for product data mining tasks. To do so, we process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating language resources: training word-embedding models, continued pre-training of BERT-like language models, and training machine translation models that are used as a proxy to generate product-related keywords. These language resources are then evaluated in three downstream tasks, product classification, linking, and fake review detection using an extensive set of benchmarks. Our results show word embeddings to be the most reliable and consistent method to improve the accuracy on all tasks (with up to 6.9% points in macro-average F1 on some datasets). Contrary to some earlier studies that suggest a rather simple but effective approach such as building domain-specific language models by pre-training using in-domain corpora, our work serves a lesson that adapting these methods to new domains may not be as easy as it seems. We further analyse our datasets and reflect on how our findings can inform future research and practice.