Challenges of Applying Knowledge Graph and their Embeddings to a Real-world Use-case
Published: 2021 Dezember
Buchtitel: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2021) co-located with the 20th International Semantic Web Conference (ISWC 2021)
Different Knowledge Graph Embedding (KGE) models have been proposed so far which are trained on some specific KG completion tasks such as link prediction and evaluated on datasets which are mainly created for such purpose. Mostly, the embeddings learnt on link prediction tasks are not applied for downstream tasks in real-world use-cases such as data available in different companies/organizations. In this paper, the challenges with enriching a KG which is generated from a real-world relational database (RDB) about companies, with information from external sources such as Wikidata and learning repre- sentations for the KG are presented. Moreover, a comparative analysis is presented between the KGEs and various text embeddings on some downstream clustering tasks. The results of experiments indicate that in use-cases like the one used in this paper, where the KG is highly skewed, it is beneficial to use text embeddings or language models instead of KGEs.