A distributed joint extraction framework for sedimentological entities and relations with federated learning

Sedimentological knowledge graphs can be used to identify natural resources in earth layers, which may help geologists analyze the distribution of oil crude in earth, and therefore locating the oilfield that is unknown. The building of such knowledge graphs mainly counts on the methods of joint extraction for pairwise entities and the corresponding relations on large-scale data. However, the whole sedimentological data is fairly owned by the different parties with the possibly inconsistent format. Centralized processing on sedimentological data as a whole will be either securely or structurally impractical. Therefore, this paper proposes a framework of distributed joint extraction in order to harvest knowledge triplets on distributed sedimentological corpus that are from many disparate sources without data transmission. The experimental studies demonstrate our methods not only approach the previous state-of-the-art but also protect the data privacy and security for data holders.