A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal
Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …
InGram: Inductive knowledge graph embedding via relation graphs
Inductive knowledge graph completion has been considered as the task of predicting
missing triplets between new entities that are not observed during training. While most …
missing triplets between new entities that are not observed during training. While most …
A survey of inductive knowledge graph completion
X Liang, G Si, J Li, P Tian, Z An, F Zhou - Neural Computing and …, 2024 - Springer
Abstract Knowledge graph completion (KGC) can enhance the completeness of the
knowledge graph (KG). Traditional transductive KGC assumes that all entities and relations …
knowledge graph (KG). Traditional transductive KGC assumes that all entities and relations …
Generalizing to unseen elements: A survey on knowledge extrapolation for knowledge graphs
Knowledge graphs (KGs) have become valuable knowledge resources in various
applications, and knowledge graph embedding (KGE) methods have garnered increasing …
applications, and knowledge graph embedding (KGE) methods have garnered increasing …
Logical reasoning with relation network for inductive knowledge graph completion
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of
newly-coming entities that never appeared in the training set. Such a setting is more in line …
newly-coming entities that never appeared in the training set. Such a setting is more in line …
Fully-inductive link prediction with path-based graph neural network: A comparative analysis
X Liang, G Si, J Li, Z An, P Tian, F Zhou - Neurocomputing, 2024 - Elsevier
Recently, fully-inductive link prediction in knowledge graphs (KGs) has aimed to predict
missing links between unseen–unseen entities, independently completing evolving KGs …
missing links between unseen–unseen entities, independently completing evolving KGs …
Dynamic link prediction: Using language models and graph structures for temporal knowledge graph completion with emerging entities and relations
Abstract Knowledge graphs (KGs) represent real-world facts through entities and relations.
However, static KGs fail to capture continuously emerging entities and relations over time …
However, static KGs fail to capture continuously emerging entities and relations over time …
Inductive reasoning with type-constrained encoding for emerging entities
C Mu, L Zhang, Z Wang, Q Yuan, C Peng - Neural Networks, 2024 - Elsevier
Abstract Knowledge graph reasoning, vital for addressing incompleteness and supporting
applications, faces challenges with the continuous growth of graphs. To address this …
applications, faces challenges with the continuous growth of graphs. To address this …
Temporal knowledge graph reasoning triggered by memories
The task of inferring missing facts using the temporal knowledge graph (TKG) is important
and has been widely studied. Extrapolation in TKG inference is more challenging because …
and has been widely studied. Extrapolation in TKG inference is more challenging because …
Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning
Abstract Real-world Temporal Knowledge Graphs keep growing with time and new entities
and facts emerge continually, necessitating a model that can extrapolate to future …
and facts emerge continually, necessitating a model that can extrapolate to future …