A survey on temporal knowledge graph completion: Taxonomy, progress, and prospects
Temporal characteristics are prominently evident in a substantial volume of knowledge,
which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia …
which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia …
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 …
Temporal knowledge graph reasoning with historical contrastive learning
Temporal knowledge graph, serving as an effective way to store and model dynamic
relations, shows promising prospects in event forecasting. However, most temporal …
relations, shows promising prospects in event forecasting. However, most temporal …
Temporal knowledge graph completion: A survey
Knowledge graph completion (KGC) can predict missing links and is crucial for real-world
knowledge graphs, which widely suffer from incompleteness. KGC methods assume a …
knowledge graphs, which widely suffer from incompleteness. KGC methods assume a …
TECHS: Temporal logical graph networks for explainable extrapolation reasoning
Extrapolation reasoning on temporal knowledge graphs (TKGs) aims to forecast future facts
based on past counterparts. There are two main challenges:(1) incorporating the complex …
based on past counterparts. There are two main challenges:(1) incorporating the complex …
Chatrule: Mining logical rules with large language models for knowledge graph reasoning
Logical rules are essential for uncovering the logical connections between relations, which
could improve the reasoning performance and provide interpretable results on knowledge …
could improve the reasoning performance and provide interpretable results on knowledge …
Back to the future: Towards explainable temporal reasoning with large language models
Temporal reasoning is a crucial natural language processing (NLP) task, providing a
nuanced understanding of time-sensitive contexts within textual data. Although recent …
nuanced understanding of time-sensitive contexts within textual data. Although recent …
Teilp: Time prediction over knowledge graphs via logical reasoning
Conventional embedding-based models approach event time prediction in temporal
knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing …
knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing …
Temporal knowledge graph forecasting without knowledge using in-context learning
Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict
future facts using knowledge of past facts. In this paper, we apply large language models …
future facts using knowledge of past facts. In this paper, we apply large language models …
Chronobridge: a novel framework for enhanced temporal and relational reasoning in temporal knowledge graphs
The task of predicting entities and relations in Temporal Knowledge Graph (TKG)
extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as …
extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as …