Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning (2104.10353v1)

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.

Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

The paper "Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning" introduces RE-GCN, a novel approach designed to address temporal knowledge graph (TKG) reasoning efficiently and effectively. TKGs are sequences of knowledge graphs (KGs) indexed by time, which introduce additional complexity over static KGs due to the necessity of temporal reasoning for predicting future events.

Motivation and Approach

The primary task is to predict missing facts in a TKG, which encompasses both entity prediction and relation prediction at future timestamps. Traditional methods often fall short in accommodating the temporal dynamics due to limitations such as the inability to model sequences of KGs together and neglecting static properties of entities.

The authors propose the RE-GCN architecture which leverages recurrent evolutional representation learning. RE-GCN's primary innovation lies in combining a relation-aware Graph Convolution Network (GCN) to capture intra-timestamp structural dependencies, with recurrent components to model inter-timestamp sequential patterns. These components work together to evolve the representations of entities and relations over time. Additionally, the model integrates static properties, using a static graph constraint to incorporate background knowledge and improve entity embeddings.

Model Components

1. Relation-Aware GCN: This component captures the structural dependencies within each temporal snapshot of the KG, considering concurrent facts and ensuring dependencies are adequately modeled.
2. Recurrent Components: Through a gate recurrent network, RE-GCN models sequential patterns of the facts observed at different timestamps. This approach emphasizes the historical context to predict future relationships and entities.
3. Static Graph Constraint: Static properties of entities, such as semantic types or categories, are used to anchor the dynamic embeddings via constraints, providing consistency across temporal predictions.

Performance and Results

The RE-GCN model exhibits substantial performance improvements across six benchmark TKG datasets, achieving up to 11.46% improvement in mean reciprocal rank (MRR) for entity prediction. Furthermore, it offers substantial efficiency gains with up to 82 times speedup compared to existing state-of-the-art models like RE-NET. Such efficiency is attributed to RE-GCN's holistic treatment of the TKG sequence, allowing joint processing of multiple queries simultaneously, as opposed to handling each query independently.

These results highlight RE-GCN's robustness in handling both entity prediction and relation prediction tasks, rendering it suitable for temporal reasoning challenges. RE-GCN's capacity to integrate static and dynamic components offers a more comprehensive understanding of TKGs, ultimately contributing to its superior performance over previous models limited by simpler temporal or static assumptions.

Implications and Future Directions

The introduction of RE-GCN not only advances the state-of-the-art in TKG reasoning but also sets the stage for future exploration into temporal dynamics in other complex data structures. As knowledge graphs grow increasingly temporal and richer in detail, architectures like RE-GCN could be pivotal in developing applications across crisis management, social network analysis, and dynamic recommendation systems.

Future developments might explore extensions that incorporate continuous time modeling, integration with larger contextual embeddings from LLMs, or generalized solutions suitable for multi-modal data involving both structured KGs and unstructured text.

In conclusion, RE-GCN marks a significant step forward in the reasoning over Temporal Knowledge Graphs, marrying efficiency with enriched representation modeling to bridge static and temporal data paradigms effectively.