Logic and Commonsense-Guided Temporal Knowledge Graph Completion

• URL: http://arxiv.org/abs/2211.16865v2
• Date: Mon, 15 May 2023 12:20:58 GMT
• Title: Logic and Commonsense-Guided Temporal Knowledge Graph Completion
• Authors: Guanglin Niu, Bo Li
• Abstract summary: A temporal knowledge graph (TKG) stores the events derived from the data involving time. We propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events.
• Score: 9.868206060374991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneously. To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense. Specifically, we design a temporal rule learning algorithm to construct a rule-guided predicate embedding regularization strategy for learning the causality among events. Furthermore, we could accurately evaluate the plausibility of events via auxiliary commonsense knowledge. The experimental results of TKGC task illustrate the significant performance improvements of our model compared with the existing approaches. More interestingly, our model is able to provide the explainability of the predicted results in the view of causal inference. The source code and datasets of this paper are available at https://github.com/ngl567/LCGE.

Related papers

• DPCL-Diff: The Temporal Knowledge Graph Reasoning based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning [3.645855411897217]
  We propose a graph node diffusion model with dual-domain periodic contrastive learning (DPCL-Diff) GNDiff introduces noise into sparsely related events to simulate new events, generating high-quality data that better conforms to the actual distribution. DPCL-Diff maps periodic and non-periodic event entities to Poincar'e and Euclidean spaces, leveraging their characteristics to distinguish similar periodic events effectively.
  arXiv  Detail & Related papers  (2024-11-03T08:30:29Z)
• 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)
• TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning [14.480267340831542]
  We propose TEILP, a logical reasoning framework that naturally integrates temporal elements into knowledge graph predictions. We first convert TKGs into a temporal event knowledge graph (TEKG) which has a more explicit representation of time in term of nodes of the graph. Finally, we introduce conditional probability density functions, associated with the logical rules involving the query interval, using which we arrive at the time prediction.
  arXiv  Detail & Related papers  (2023-12-25T21:54:56Z)
• Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation [59.417443739208146]
  We propose a new event forecasting model based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities. We evaluate our proposed model on five benchmark graphs.
  arXiv  Detail & Related papers  (2023-08-29T03:26:38Z)
• Generic Temporal Reasoning with Differential Analysis and Explanation [61.96034987217583]
  We introduce a novel task named TODAY that bridges the gap with temporal differential analysis. TODAY evaluates whether systems can correctly understand the effect of incremental changes. We show that TODAY's supervision style and explanation annotations can be used in joint learning.
  arXiv  Detail & Related papers  (2022-12-20T17:40:03Z)
• Temporal Knowledge Graph Reasoning with Historical Contrastive Learning [24.492458924487863]
  We propose a new event forecasting model called Contrastive Event Network (CENET) CENET learns both the historical and non-historical dependency to distinguish the most potential entities that can best match the given query. During the inference process, CENET employs a mask-based strategy to generate the final results.
  arXiv  Detail & Related papers  (2022-11-20T08:32:59Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• Learning Temporal Rules from Noisy Timeseries Data [72.93572292157593]
  We focus on uncovering the underlying atomic events and their relations that lead to the composite events within a noisy temporal data setting. We propose a Neural Temporal Logic Programming (Neural TLP) which first learns implicit temporal relations between atomic events and then lifts logic rules for supervision.
  arXiv  Detail & Related papers  (2022-02-11T01:29:02Z)
• Online Learning Probabilistic Event Calculus Theories in Answer Set Programming [70.06301658267125]
  Event Recognition (CER) systems detect occurrences in streaming time-stamped datasets using predefined event patterns. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of rules weighted in the Event Calculus. Our results demonstrate the superiority of our novel approach, both terms efficiency and predictive.
  arXiv  Detail & Related papers  (2021-03-31T23:16:29Z)
• Temporal Reasoning on Implicit Events from Distant Supervision [91.20159064951487]
  We propose a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events. We find that state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. We propose a neuro-symbolic temporal reasoning model, SYMTIME, which exploits distant supervision signals from large-scale text and uses temporal rules to infer end times.
  arXiv  Detail & Related papers  (2020-10-24T03:12:27Z)

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.