A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution

• URL: http://arxiv.org/abs/2108.13024v1
• Date: Mon, 30 Aug 2021 07:27:19 GMT
• Title: A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution
• Authors: Kangzheng Liu and Yuhong Zhang
• Abstract summary: A temporal knowledge graph completion (KGC) method is proposed based on the direct encoding time information framework. A large number of experiments on temporal knowledge graph datasets extracted from the real world demonstrate the effectiveness of our method.
• Score: 1.3071779090051663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Completion through the embedding representation of the knowledge graph (KGE) has been a research hotspot in recent years. Realistic knowledge graphs are mostly related to time, while most of the existing KGE algorithms ignore the time information. A few existing methods directly or indirectly encode the time information, ignoring the balance of timestamp distribution, which greatly limits the performance of temporal knowledge graph completion (KGC). In this paper, a temporal KGC method is proposed based on the direct encoding time information framework, and a given time slice is treated as the finest granularity for balanced timestamp distribution. A large number of experiments on temporal knowledge graph datasets extracted from the real world demonstrate the effectiveness of our method.

Related papers

• PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation [49.00940417190911]
  Existing graph representation learning models encounter a catastrophic problem when learning with newly incoming graphs. In this paper, we propose a PUdo-label guided Memory bAnkrogation (PUMA) framework to enhance its efficiency and effectiveness.
  arXiv  Detail & Related papers  (2023-12-22T05:09:58Z)
• Temporal Smoothness Regularisers for Neural Link Predictors [8.975480841443272]
  We show that a simple method like TNTComplEx can produce significantly more accurate results than state-of-the-art methods. We also evaluate the impact of a wide range of temporal smoothing regularisers on two state-of-the-art temporal link prediction models.
  arXiv  Detail & Related papers  (2023-09-16T16:52:49Z)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning [47.52733127616005]
  We propose a Frame-level Timeline Modeling (FTM) method that helps to capture both short-term and long-term features. Our method can be easily assembled with most temporal GNNs.
  arXiv  Detail & Related papers  (2023-02-23T06:53:16Z)
• A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs [18.451872649228196]
  We propose a simple graph neural network (GNN) model combined with a temporal information matching mechanism. We also propose a method to generate unsupervised alignment seeds via the temporal information of TKG.
  arXiv  Detail & Related papers  (2022-09-20T12:27:34Z)
• Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations [1.8262547855491458]
  We introduce Time-LowFER, a family of parameter-efficient and time-aware extensions of the low-rank tensor factorization model LowFER. Noting several limitations in current approaches to represent time, we propose a cycle-aware time-encoding scheme for time features. We implement our methods in a unified temporal knowledge graph embedding framework, focusing on time-sensitive data processing.
  arXiv  Detail & Related papers  (2022-04-10T22:24:11Z)
• Temporal Knowledge Graph Completion: A Survey [24.35073672695095]
  Knowledge graph completion (KGC) can predict missing links and is crucial for real-world knowledge graphs. Recent methods have shown improved predictive results by further incorporating the timestamps of facts.
  arXiv  Detail & Related papers  (2022-01-16T05:43:49Z)
• One-shot Learning for Temporal Knowledge Graphs [49.41854171118697]
  We propose a one-shot learning framework for link prediction in temporal knowledge graphs. Our proposed method employs a self-attention mechanism to effectively encode temporal interactions between entities. Our experiments show that the proposed algorithm outperforms the state of the art baselines for two well-studied benchmarks.
  arXiv  Detail & Related papers  (2020-10-23T03:24:44Z)
• From Static to Dynamic Node Embeddings [61.58641072424504]
  We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropriate graph time-series representation. We find that the top-3 temporal models are always those that leverage the new $epsilon$-graph time-series representation.
  arXiv  Detail & Related papers  (2020-09-21T16:48:29Z)
• Time-aware Graph Embedding: A temporal smoothness and task-oriented approach [9.669206664225234]
  This paper presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness. At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding. Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally-aware information.
  arXiv  Detail & Related papers  (2020-07-22T02:20:25Z)
• Block-Approximated Exponential Random Graphs [77.4792558024487]
  An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. We propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions. Our methods are scalable to sparse graphs consisting of millions of nodes.
  arXiv  Detail & Related papers  (2020-02-14T11:42:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.