Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

• URL: http://arxiv.org/abs/2104.10353v1
• Date: Wed, 21 Apr 2021 05:12:21 GMT
• Title: Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
• Authors: Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang and Xueqi Cheng
• Abstract summary: Key to predict future facts is to thoroughly understand the historical facts. A TKG is actually a sequence of KGs corresponding to different timestamps. We propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN)
• Score: 59.004025528223025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future is still far from resolved. The key to predict future facts is to thoroughly understand the historical facts. A TKG is actually a sequence of KGs corresponding to different timestamps, where all concurrent facts in each KG exhibit structural dependencies and temporally adjacent facts carry informative sequential patterns. To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently. Specifically, for the evolution unit, a relation-aware GCN is leveraged to capture the structural dependencies within the KG at each timestamp. In order to capture the sequential patterns of all facts in parallel, the historical KG sequence is modeled auto-regressively by the gate recurrent components. Moreover, the static properties of entities such as entity types, are also incorporated via a static graph constraint component to obtain better entity representations. Fact prediction at future timestamps can then be realized based on the evolutional entity and relation representations. Extensive experiments demonstrate that the RE-GCN model obtains substantial performance and efficiency improvement for the temporal reasoning tasks on six benchmark datasets. Especially, it achieves up to 11.46\% improvement in MRR for entity prediction with up to 82 times speedup comparing to the state-of-the-art baseline.

Related papers

• Learning Granularity Representation for Temporal Knowledge Graph Completion [2.689675451882683]
  Temporal Knowledge Graphs (TKGs) incorporate temporal information to reflect the dynamic structural knowledge and evolutionary patterns of real-world facts. This paper proposes textbfLearning textbfGranularity textbfRepresentation (termed $mathsfLGRe$) for TKG completion. It comprises two main components: Granularity Learning (GRL) and Adaptive Granularity Balancing (AGB)
  arXiv  Detail & Related papers  (2024-08-27T08:19:34Z)
• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning [3.3571415078869955]
  This paper proposes an innovative reasoning approach that focuses on Learning Multi-graph Structure (LMS) LMS incorporates an adaptive gate for merging entity representations both along and across timestamps effectively. It also integrates timestamp semantics into graph attention calculations and time-aware decoders.
  arXiv  Detail & Related papers  (2023-12-04T08:23:09Z)
• Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning [25.84105067110878]
  Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information. We propose a novel architecture modeling with relation feature of TKG, namely aDAptivE path-MemOry Network (DaeMon) DaeMon adaptively models the temporal path information between query subject and each object candidate across history time.
  arXiv  Detail & Related papers  (2023-04-25T06:33:08Z)
• Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment [53.72873672076391]
  Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information. We propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information. S2T achieves at most 10.13% performance improvement compared with the state-of-the-art competitors on several datasets.
  arXiv  Detail & Related papers  (2023-02-15T06:36:04Z)
• HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning [59.38797474903334]
  This paper proposes the textbfHistorical textbfStructure textbfMatching (textbfHiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-10-18T09:39:26Z)
• Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network [111.67744771462873]
  This paper proposes a novel explainable model for sparse Knowledge Graphs (KGs) It combines high-order reasoning into a graph convolutional network, namely HoGRN. It can not only improve the generalization ability to mitigate the information insufficiency issue but also provide interpretability.
  arXiv  Detail & Related papers  (2022-07-14T10:16:56Z)
• Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning [60.94357727688448]
  TKG reasoning aims to predict potential facts in the future given the historical KG sequences. The evolutional patterns are complex in two aspects, length-diversity and time-variability. We propose a new model, called Complex Evolutional Network (CEN), which uses a length-aware Convolutional Neural Network (CNN) to handle evolutional patterns of different lengths.
  arXiv  Detail & Related papers  (2022-03-15T11:02:55Z)
• EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs [25.408246523764085]
  Reasoning over temporal knowledge graphs (TKGs) is crucial for many applications to provide intelligent services. We present a problem formulation that unifies the two major problems that need to be addressed for an effective reasoning over TKGs, namely, modeling the event time and the evolving network structure. Our proposed method EvoKG jointly models both tasks in an effective framework, which captures the ever-changing structural and temporal dynamics in TKGs.
  arXiv  Detail & Related papers  (2022-02-15T18:49:53Z)
• T-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion [13.209193437124881]
  Temporal knowledge graphs (TKGs) inherently reflect the transient nature of real-world knowledge, as opposed to static knowledge graphs. We propose T-GAP, a novel model for TKG completion that maximally utilizes both temporal information and graph structure in its encoder and decoder. Our experiments demonstrate that T-GAP achieves superior performance against state-of-the-art baselines, and competently generalizes to queries with unseen timestamps.
  arXiv  Detail & Related papers  (2020-12-19T04:45:32Z)

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.