  Most of the existing techniques to product discovery rely on syntactic
approaches, thus ignoring valuable and specific semantic information of the
underlying standards during the process. The product data comes from different
heterogeneous sources and formats giving rise to the problem of
interoperability. Above all, due to the continuously increasing influx of data,
the manual labeling is getting costlier. Integrating the descriptions of
different products into a single representation requires organizing all the
products across vendors in a single taxonomy. Practically relevant and quality
product categorization standards are still limited in number; and that too in
academic research projects where we can majorly see only prototypes as compared
to industry. This work presents a cost-effective solution for e-commerce on the
Data Web by employing an unsupervised approach for data classification and
exploiting the knowledge graphs for matching. The proposed architecture
describes available products in web ontology language OWL and stores them in a
triple store. User input specifications for certain products are matched
against the available product categories to generate a knowledge graph. This
mullti-phased top-down approach to develop and improve existing, if any,
tailored product recommendations will be able to connect users with the exact
product/service of their choice.
