CLASS MATE: Cross-Lingual Semantic Search for Material Science Driven by Knowledge Graphs

As diverse linguistic backgrounds contribute valuable insights to scientific research, effective Cross-Lingual Semantic Search (CLSS) mechanisms, which often remain overlooked, become crucial. This paper introduces CLASS MATE(https://lass-kg.demos.dice-research.org/)-a CLSS application working over material science knowledge graphs (KGs). Our work aims to bridge the digital language divide in the research community by employing advanced knowledge representation techniques. In particular, (1) we acquire our KG containing chemical substances with multilingual entity labels; (2) we implement a symbolic similarity-based named entity recognition algorithm; and (3) we develop a demo application employing the previous steps for retrieving information requested by a user from our KG and LOD sources in multiple languages. Our industry partner Springer Nature provided us with a KG as an information source to understand information needs. To the best of our knowledge, we made the first contribution to CLSS within material science.