Integrating Product Classification Standards into eCl@ss and UNSPSC on the Web of Data

This paper details a novel approach for using product classification standards in Web data markup in Microdata and JSON-LD syntax that does not require the availability of proper Web ontology variants of the underlying standards. Our proposal has already been integrated into the official version of (in use by Google and major search engine operators) and can be readily used for research and business applications.

Deep Product Comparison on the Semantic Web

In this thesis, Alex Stolz analyzes how the Semantic Web and its growing amount of structured data on the basis of the GoodRelations and vocabularies can be used to provide a better product search paradigm and interaction model for the Web, and improved, data-driven e-commerce in general. He identifies five core problems and describes theoretically sound and practically usable solutions to each of them, namely (1) an efficient crawling method that can deal with the fact that relevant markup is found in the deep branches of e-commerce Web sites, (2) a conceptual approach and toolchain for integrating product model master data from PIM/PDM/PLM spheres, (3) a method for harvesting product category information from standards like eCl@ss and the UNSPSC, (4) data quality management, and (5) an interaction model and user interface that supports the incremental discovery of the product option space. The work is the first comprehensive analysis of this topic and is suited for researchers and practitioners alike. Starting with a comprehensive survey of the state of the art, it first analyzes the problems at a conceptual level and then presents a state-of-the-art implementation of several prototypes as a proof of concept. All software and data described in the thesis are available online and can serve as valuable input for future work in industry and academia.