Nora Engleitner, Werner Kreiner, Nicole Schwarz, Theodorich Kopetzky, Lisa Ehrlinger,
"Knowledge Graph Embeddings for News Article Tag Recommendation"
, in Ilaria Tiddi, Maria Maleshkova, Tassilo Pellegrini, Victor de Boer: Joint Proceedings of the Semantics co-located events: Poster&Demo track and Workshop on Ontology-Driven Conceptual Modelling of Digital Twins, Serie CEUR Workshop Proceedings, Vol. 2941, Sun SITE Central Europe, Aachen, 9-2021
Original Titel:
Knowledge Graph Embeddings for News Article Tag Recommendation
Sprache des Titels:
Englisch
Original Buchtitel:
Joint Proceedings of the Semantics co-located events: Poster&Demo track and Workshop on Ontology-Driven Conceptual Modelling of Digital Twins
Original Kurzfassung:
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.
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