Knowledge Graph Embeddings for News Article Tag Recommendation

Newsadoo is a media startup that provides news articles from different sources on a single platform. Users can create individual timelines, where they follow the latest development of a specific topic. To support the topic creation process, we developed an algorithm that automatically suggests related tags to a set of given reference tags. In this paper, we first introduce the Newsadoo tag recommendation system, which consists of three components: (1) item-based similarity, (2) knowledge graph similarity, and (3) actuality. We describe the knowledge graph component in more detail and analyze the suitability of different knowledge graphs and embedding techniques to enhance the quality of the overall Newsadoo tag recommendation. The paper concludes with a list of lessons learned and interesting future work.