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Knowledge Graphs offer a very useful and powerful structure for representinginformation, consequently, they have been adopted as the backbone for manyapplications in e-commerce scenarios. In this paper, we describe an applicationof existing techniques for enriching thelarge-scale Fashion Knowledge Graph(FKG) that we build at Farfetch. In particular, we apply techniques for namedentity recognition (NER) and entity linking (EL) in order to extract and linkrich metadata from product textual descriptions to entities in the FKG. Havinga complete and enriched FKG as an e-commerce backbone can have a highlyvaluable impact on downstream applications such as search and recommendations.However, enriching a Knowledge Graph in the fashion domain has its ownchallenges. Data representation is different from a more generic KG, likeWikidata and Yago, as entities (e.g. product attributes) are too specific tothe domain, and long textual descriptions are not readily available. Dataitself is also scarce, as labelling datasets to train supervised models is avery laborious task. Even more, fashion products display a high variability andrequire an intricate ontology of attributes to link to. We use a transferlearning based approach to train an NER module on a small amount of manuallylabeled data, followed by an EL module that links the previously identifiednamed entities to the appropriate entities within the FKG. Experiments using apre-trained model show that it is possible to achieve 89.75% accuracy in NEReven with a small manually labeled dataset. Moreover, the EL module, despiterelying on simple rule-based or ML models (due to lack of training data), isable to link relevant attributes to products, thus automatically enriching theFKG.

Enriching a Fashion Knowledge Graph from Product Textual Descriptions