In a drive to reduce the carbon footprint of their customers’ Grundfos is revolutionizing how they engage on efficiency savings of their cooling systems. Initial communication with the customers about cooling systems needs to capture complex system information, yet be simple and relevant for each of the many customer profiles to maximize retention. The solution has to work with a wide variety of engineering systems and many stakeholders within the client.
Matching tables against Knowledge Graphs is a crucial task in many applications. A widely adopted solution to improve the precision of matching algorithms is to refine the set of candidate entities by their type in the Knowledge Graph. However, it is not rare that a type is missing for a given entity. In this paper, we propose a methodology to improve the refinement phase of matching algorithms based on type prediction and soft constraints. We apply our methodology to state-of-the-art algorithms, showing a performance boost on different datasets.
Graph Neural Networks (GNNs) have emerged as a mature AI approach used by companies for Knowledge Graph enrichment via text processing for news classification, question and answer, search result organization, and much more. During this presentation we will discuss the advantages of GNNs for text classification and relationship extraction for Enterprise Knowledge Graphs.