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 …
Learning latent relations for temporal knowledge graph reasoning
Abstract Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on
historical data. However, due to the limitations in construction tools and data sources, many …
historical data. However, due to the limitations in construction tools and data sources, many …
Towards benchmarking and improving the temporal reasoning capability of large language models
Reasoning about time is of fundamental importance. Many facts are time-dependent. For
example, athletes change teams from time to time, and different government officials are …
example, athletes change teams from time to time, and different government officials are …
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 …
A review of graph neural networks and pretrained language models for knowledge graph reasoning
J Ma, B Liu, K Li, C Li, F Zhang, X Luo, Y Qiao - Neurocomputing, 2024 - Elsevier
Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …
structure but faces challenges such as incomplete construction or inability to handle new …
Learning long-and short-term representations for temporal knowledge graph reasoning
Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on
historical TKG data. Most of the existing methods are incapable of explicitly modeling the …
historical TKG data. Most of the existing methods are incapable of explicitly modeling the …
Multi-granularity temporal question answering over knowledge graphs
Recently, question answering over temporal knowledge graphs (ie, TKGQA) has been
introduced and investigated, in quest of reasoning about dynamic factual knowledge. To …
introduced and investigated, in quest of reasoning about dynamic factual knowledge. To …
[HTML][HTML] Temporal knowledge graph question answering via subgraph reasoning
Abstract Knowledge graph question answering (KGQA) has recently received a lot of
attention and many innovative methods have been proposed in this area, but few have been …
attention and many innovative methods have been proposed in this area, but few have been …
Deep purified feature mining model for joint named entity recognition and relation extraction
Y Wang, Y Wang, Z Sun, Y Li, S Hu, Y Ye - Information Processing & …, 2023 - Elsevier
Table filling based joint named entity recognition and relation extraction task aims to share
representation of subtasks in a table to extract structured knowledge. However, most of …
representation of subtasks in a table to extract structured knowledge. However, most of …