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5 days ago · Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous …

Jan 3, 2025 · Static knowledge graph completion (KGC) has made significant progress in the field of artificial intelligence. However, knowledge is time-sensitive, thus the introduction of …

With this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowl-edge graph that KGC methods fail to capture. In this paper, for the first time, we …

Aug 13, 2024 · Temporal Knowledge Graph Completion (TKGC) aims to infer missing facts in Temporal Knowledge Graphs (TKGs), where facts are stored along with significant temporal …

Knowledge graph completion (KGC) [Lin et al., 2015] aims at automatically inferring missing facts for a knowl-edge graph by learning from existing facts (also known as link prediction).

Aug 19, 2023 · Temporal Knowledge Graph Completion (TKGC) aims to discover missing factual knowledge from a given temporal knowledge graph. Previous works usually design Temporal …

Aug 4, 2023 · Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this …

Sep 20, 2024 · Most TKG completion methods heavily rely on the repetitive appearance of historical entities, which poses a challenge for completing entities in the same period. …

The Temporal Knowledge Graph (TKG) extends traditional knowledge graphs by introducing a temporal dimension, but the challenges of temporal dependency and sequential characteristics …

Temporal Knowledge Graph Completion (TKGC) involves predicting and filling in missing facts within time series data, a crucial task with wide-ranging applications across various domains.

Apr 3, 2020 · In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of …

2 days ago · Temporal knowledge graph reasoning aims to address the incompleteness of temporal knowledge graphs by reasoning unknown facts from known facts. Multi-hop path …

Aug 27, 2024 · Temporal Knowledge Graphs (TKGs) incorporate temporal information to reflect the dynamic structural knowledge and evolutionary patterns of real-world facts. Nevertheless, …

5 days ago · In this paper, we present a novel time-aware knowledge graph embebdding approach, TeLM, which performs 4th-order tensor factorization of a Temporal knowledge …

Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized …

Aug 14, 2021 · In this paper, we propose T-GAP, a novel model for TKG completion that maximally utilizes both temporal information and graph structure in its encoder and decoder.

6 days ago · Reasoning over Temporal Knowledge Graphs (TKGs) presents challenges in modeling the dynamic relationships and evolving behaviors of entities and relations over time. …

A Knowledge Graph (KG), which structurally represents entities (nodes) and relationships (edges), offers a powerful and flexible approach to knowledge representation in the field of …

5 days ago · Numerous Temporal Knowledge Graph Completion (TKGC) methods have been proposed by mapping the entities and relations in TKG to the high-dimensional representations.

Aug 13, 2024 · Temporal graph entity alignment aims at finding the equivalent entity pairs across different temporal knowledge graphs (TKGs). Primarily methods mainly utilize a time-aware …

Mar 19, 2025 · Next, a temporal graph convolutional network (T-GCN) cell is developed to quantify the relationships between spatio-temporal features, environmental knowledge, and …

2 days ago · Recently, various types of tensor decomposition methods have been proposed to leverage prior knowledges, such as temporal knowledge, correlations between dimensions, as …