An estimated 2 million people contract health-care associated infections on a yearly basis, according to the CDC, resulting in nearly 100,000 deaths and, per the National Center for Biotechnology Information, between $30 billion to $45 billion in expenses to hospitals nationwide. The CDC-funded "Benefits of Enhanced Terminal Room Disinfection" study found the use of a disinfection robot may have a major impact on those numbers, with a robot cleaning "decreas(ing) the relative risk of colonization and infection of target multi-drug resistant organisms among patients admitted to the same room by a cumulative 30% in a hospital setting, with 93% compliance of standard disinfection protocols."

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective --- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.