# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 2 Related Works  
### KGE Models  
According to KGE models' input data, the KGE models currently in use can be broadly split into two main streams:  
1. The KG structural information-based methods include translation-based and semantic matching models. Translation-based models [10; 12; 15] are models that utilize entity and relation embeddings to compute translation scores, where relations represent the translation operations between entities. Translating embeddings for modeling multi-relational data (TransE) [10] is the pioneer of the translation-based model, which embeds entities and relations into the space with the same dimension, and regards relations as translation operations between entity vectors. The advantage of TransE is that it is simple and efficient, while it cannot model various relation patterns. Knowledge graph embedding by relational rotation in complex space (RotatE) [15] embeds entities and relations in a complex vector space, treats relations as rotation operations between entity vectors, effectively models and infers various relational patterns, and refreshes the best results of the KGC task. Learning hierarchy-aware knowledge graph embeddings for link prediction (HAKE) [12] effectively embeds the semantic hierarchy by mapping the entity to the polar coordinate system, achieving the STOA result of the KGC task. Semantic matching models [11; 13; 14; 16] compute semantic matching scores for entity and relation embeddings in the latent space. A three-way model for collective learning on multi-relational data (RESCAL) [16] treats entities as vectors and relations as matrices and calculates scores with bilinear functions. Embedding entities and relations for learning and inference in knowledge bases (DistMult) [13] simplifies RESCAL by restricting the relation matrix to be a diagonal matrix. Complex embeddings for simple link prediction (ComplEx) [14] extend DistMult to embed entities and relations into complex space. Quaternion knowledge graph embeddings (QuatE) [11] embed a hypercomplex value with three imaginary components to represent entities and model the relation as a rotation on a 4-dimensional space (hypercomplex space), thus unifying ComplEx [14] and RotatE [15].
2. External information-based methods focus on adding extra information to enrich the KGE models. Most models that add external information [18; 20; 21] utilize the logic rules mined from the knowledge graph to improve the link prediction results. Fast rule mining in ontological knowledge bases with AMIE + + (AMIE) [20], End-to-end differentiable rule mining on knowledge graphs (DRUM) [21], and Knowledge graph embedding with iterative guidance from soft rules (RUGE) [18] automatically mine the logic rules in KG and apply the logic rules to KGC tasks. A considerable part of the models that add internal information [17; 19; 22] use the known concept information corresponding to the entity to improve the link prediction outcomes. Representation learning of knowledge graphs with hierarchical types (TKRL) [17] uses the type information of the entity to design a scoring function with a hierarchical projection matrix for the entity, which improves the performance of the KGC task, Type-based multiple embedding representations for knowledge graph completion (TransT) [22] adopts entity types to construct relation types and takes the similarity between relative entities and relations as prior knowledge, and utilizes prior knowledge to improve the KGC task results. A scalable commonsense-aware framework for multi-view knowledge graph completion (CAKE) [19] leverages commonsense concepts of entities to improve the quality of negative sampling and the accuracy of link prediction candidate entities. Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding (OntoEA) [25] embeds ontology and knowledge graphs together to get better entity embedding.