# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
## 2 Related Works
### Relation Prediction Methods  
In order to increase the completeness of KGs, state-of-the-art methods use a KG internal structure [8; 15] or external KG information [22; 23]. In this work, we focused on the former and studied the relation prediction problem.  
**Transductive methods**. Transductive methods are used to learn an entity-specific embedding for each node, which have one thing in common: reasoning over original KGs. However, it is difficult to predict the missing links between unseen nodes. For example, TransE [8] is based on translation while RGCN [10] and CompGCN [11] are based on GNN. The major differences between them are the scoring function and whether the structure information in KGs is utilized. [PERSON] et al. proposed to employ a global neighborhood aggregator to obtain the global structural information of an entity to solve the problem of sparse local structural information under certain snapshots [24]. [PERSON] et al. proposed a multi-hop path inference model based on sparse temporal knowledge graphs [25]. Recently, [PERSON] et al. proposed knowledge graph completion for multi-level interactions, where entities and relations interact simultaneously at both fine- and coarse-grain levels [26].  
**Inductive methods**. Inductive methods can be used to learn how to reason on unseen nodes. There are two main categories of methods: rule-based and graph-based. Rule-based methods aim to learn entity-independent logical rules of reasoning. For instance, NeuralLP [14] and DRUM [27] integrate neural networks with symbolic rules to learn logic rules and rule confidence in an end-to-end microscopic manner.  
Concerning graph-based methods, in recent years, researchers have drawn inspiration from the local information aggregation capability of graph neural networks, and they have incorporated graph neural networks (GNNs) into their models. GraIL [15] acquires the topology of target nodes by extracting the enclosing subgraph of the target triad, thus exhibiting inductive prediction abilities. Building on this model, TACT [16] introduces the correlation of relations in subgraphs and constructs a relational correlation network (RCN) to enhance the encoding of subgraphs. CoMPLIE [17] proposes a node-edge communication message propagation network to enhance the interaction between nodes and edges, and it naturally deals with asymmetric or anti-symmetric relations so as to enhance the adequate flow of relational information. ConGLR [13] formulates a contextual graph that represents the relationship paths in the subgraph, whereby two GCNs are applied to deal with the enclosing subgraphs and contexts, in which different layers utilize counterpart outputs interactively for better feature representation. RE-PORT [28] aggregates relational paths and contexts to capture the connections and intrinsic nature of entities through a unified hierarchical transformation framework. However, since the experimental metrics of RE-PORT are different from those of other state-of-the-art models, the RE-PORT model was not chosen as a comparison model in the experimental part of this paper. RMPI [29] uses a novel relational message passing network for fully inductive knowledge graph completion. SNRI [21] extracts the neighbor relational features and path embeddings of nodes to make full use of the full neighbor relational information of the entities to obtain a better inductive effect. However, all these methods only add additional simple processing load, and they do not take full advantage of the whole structural feature of KGs. Different from SNRI, CNIA keeps integrated neighboring relations, utilizes a dual attention mechanism to process the structural feature of subgraphs, and it introduces commonsense re-ranking.