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This paper presents the results of the sixth edition of the BioASQ challenge. The BioASQ challenge aims at the promotion of systems and methodologies through the organization of a challenge on two tasks: semantic indexing and question answering. In total, 26 teams with more than 90 systems participated in this year's challenge. As in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the-art systems are continuously improving, pushing the frontier of research.

The goal of the BioASQ challenge is to engage researchers into creating cuttingedge biomedical information systems. Specifically, it aims at the promotion of systems and methodologies that are able to deal with a plethora of different tasks in the biomedical domain. This is achieved through the organization of challenges. The fifth challenge consisted of three tasks: semantic indexing, question answering and a new task on information extraction. In total, 29 teams with more than 95 systems participated in the challenge. Overall, as in previous years, the best systems were able to outperform the strong baselines. This suggests that stateof-the art systems are continuously improving, pushing the frontier of research.

Knowledge Graphs provide insights from data extracted in various domains. In this paper, we present a new approach to discover probable drug-to-drug interactions, through the generation of a Knowledge Graph from disease-specific literature. The Graph is generated using natural language processing and semantic indexing of open biomedical publications and manually annotated resources. Then, the semantic paths connecting different drugs in the Graph are extracted and aggregated into feature vectors representing drug pairs. Finally, a classifier is trained on known interactions and is used to discover other possible interacting drug pairs. We evaluate this approach on two use cases, Alzheimer's Disease and Lung Cancer. A manually curated drug database is utilized as a golden standard and our system is shown to outperform competing graph embedding approaches, while also recommending new drug-drug interactions that are validated retrospectively.