Fig 1.
An example of a bipartite network (U, V, E) and two of its bi-cliques.
The nodes in blue form the node set U, and the nodes in red form the node set V. The two sub-networks framed in green and purple are two maximal bi-cliques of the network.
Fig 2.
Left: A sample formal context. Right: The concept lattice corresponding to the formal context in the left panel. The nodes in yellow are neighbors and the nodes in red are not neighbors.
Fig 3.
A depiction of the equivalence between bipartite networks and formal contexts, as well as the equivalence between maximal bi-cliques and formal concepts.
The bipartite network to the left can be represented as the formal context to the right. The sub-network circled in purple and green are maximal bi-cliques in the bipartite networks to the left, which can be represented into two formal concepts framed in the corresponding colors in the formal context to the right.
Fig 4.
An overview of the working flow of our method.
Fig 5.
The comparison of how much information from a concept lattice is learned and used when predicting an object by two methods, with object2vec shown on the left and BERT4FCA shown on the right.
The target object to be predicted is circled in blue. The information used for predicting the object is shown in red.
Table 1.
The features of the datasets.
Table 2.
The results for the O-O tasks.
Table 3.
The results for the O-A tasks.
Table 4.
The results for the first supplementary experiment.
Table 5.
The results for the O-O tasks of the second ablation experiment.
Table 6.
The results for the O-A tasks of the second ablation experiment.