Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction

Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs

“…GraIL [Teru et al, 2020], CoMPILE [Mai et al, 2021], TACT , ConGLR [Lin et al, 2022] where f denotes the learnable encoder, and r denotes relation embedding or relation-dependent parameters. Note that not all methods encode both head and tail entities.…”

“…TACT considers correlations between relations in a subgraph, and encodes a Relational Correlation Network (RCN) to enhance the encoding of the enclosing subgraph. ConGLR [Lin et al, 2022] formulates a context graph representing relational paths from a subgraph and then uses two GCNs to process it with the enclosing subgraph respectively. Since extracted enclosing subgraphs can be sparse and some surrounded relations are neglected, SNRI leverages complete neighbor relations of entities in a subgraph by neighboring relational features and neighboring relational paths.…”

“…EntMEAN [Hamaguchi et al, 2017] MEAN[Hamaguchi et al, 2017], LAN,VN Network [He et al, 2020],INDIGO [Liu et al, 2021] yEnt GraIL[Teru et al, 2020] GraIL[Teru et al, 2020], CoMPILE[Mai et al, 2021], TACT, ConGLR[Lin et al, 2022], SNRI [Xu et al, 2022], BertRL [Zha et al, 2022], RMPI [Geng et al, 2023], MorsE [Chen et al, 2022b], NodePiece [Galkin et al, 2022a], NBFNet [Zhu et al, 2021], RED-GNN [Zhang and Yao, 2022], INDIGO [Liu et al, 2021], RuleN [Meilicke et al, 2018], Nerual LP [Yang et al, 2017], DRUM [Sadeghian et al, 2019], CBGNN [Yan et al, 2022] Xiong et al, 2018], MetaR [Chen et al, 2019], FSRL [Zhang et al, 2020], FAAN [Sheng et al, 2020], GANA [Niu et al, 2021], MetaP [Jiang et al, 2021], P-INT [Xu et al, 2021], CSR [Huang et al, 2022] { Rel ZSGAN [Qin et al, 2020] ZSGAN [Qin et al, 2020], OntoZSL [Geng et al, 2021], DOZSL [Geng et al, 2022] DMoG [Song et al, 2022], HAPZSL [Li et al, 2022] The usage of four commonly used benchmarks described in §4. The column 'Type' indicates the dataset is used for entity or relation extrapolation.…”

Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism

“…Following GraIL, CoMPILE [10] uses communicative message-passing GNNs to extract directed enclosing subgraphs for each triplet in inductive link prediction tasks. Graph convolutional network (GCN)-based models like ConGLR [9], INDIGO [29], and LogCo [19] are also applied to inductive link prediction. ConGLR combines context graphs with logical reasoning, LogCo integrates logical reasoning and contrastive representations into GCNs, and INDIGO transparently encodes the input graph into a GCN for inductive link prediction.…”

“…Each version of each dataset contains a training graph and an inference graph, whereas the entity set of the two graphs are disjoint. Detailed statistics on the number of entities, triples and relation types of the datasets are summarized in many papers, such as [7]- [9]. For simplicity, we do not repeat the statistics in this paper again.…”