Diachronic Embedding for Temporal Knowledge Graph Completion (1907.03143v1)

Abstract: Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. 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 entities at any point in time. This is in contrast to the existing temporal KG embedding approaches where only static entity features are provided. The proposed embedding function is model-agnostic and can be potentially combined with any static model. We prove that combining it with SimplE, a recent model for static KG embedding, results in a fully expressive model for temporal KG completion. Our experiments indicate the superiority of our proposal compared to existing baselines.

• The paper proposes a diachronic entity embedding method that integrates time-dependent features into static KG models.
• It demonstrates that the DE-SimplE model achieves full expressivity and higher inference accuracy on benchmarks like ICEWS and GDELT.
• Experimental ablation studies confirm that incorporating temporal evolution enhances the robustness and adaptability of KG completion tasks.

Overview of Diachronic Embedding for Temporal Knowledge Graph Completion

The paper entitled "Diachronic Embedding for Temporal Knowledge Graph Completion" presents a novel approach to address the incompleteness of Knowledge Graphs (KGs) that involve temporal facts. Unlike previous methods primarily focused on static representations, this work introduces a temporal dimension to entity embeddings, enhancing the expressiveness and applicability of Knowledge Graph Completion (KGC) techniques for evolving datasets.

Problem Setting

Knowledge Graphs are complex structures where entities and their relationships are recorded, often necessitating temporal information to fully model the dynamics of the relationships. Existing KGC techniques have predominantly dealt with static KGs, ignoring temporal aspects vital for accurate knowledge inference over time. The challenge lies in efficiently encoding the temporal evolution of entities to improve the accuracy of inferred facts.

Proposed Contribution

The authors propose a diachronic entity embedding (DE) function, which is a model-agnostic enhancement capable of integrating temporal features into any existing static KG embedding model. This approach involves adapting the diachronic concept, often used in word embeddings, to reflect how entities' features evolve over time. This strategic inclusion of time-dependent characteristics allows the representation of entities to vary, providing a more nuanced and expressive model for Temporal KG Completion (TKGC).

Theoretical Insights

The paper provides theoretical evidence demonstrating the full expressivity of the proposed DE when incorporated into SimplE, a state-of-the-art static KG embedding model. Demonstrating full expressivity means that the model has the potential to correctly distinguish between true and false tuples across any temporal dataset, given the right parameter settings. This is a significant confirmation, implying that DE-SimplE can capture the complete semantics of temporal knowledge graphs when appropriately trained.

Empirical Evaluation

The authors validate their approach using multiple datasets such as ICEWS14, ICEWS05-15, and GDELT. Across these benchmarks, they compare several baseline methods, both static and temporal, demonstrating that the diachronic entity embedding enhances the performance metrics substantially. DE-SimplE, with its diachronic capabilities, consistently outperforms competitors, reinforcing the effectiveness of incorporating temporal dynamics in embeddings.

Model Variants and Ablation Studies

The paper includes extensive experiments to assess the impact of different design choices within the diachronic embedding framework. Variations like the inclusion of time-dependent relation embeddings, as well as experiments with different activation functions, are explored. This exhaustive paper sheds light on how these factors affect the model’s performance, informing optimal configurations for various datasets. The diachronic approach's capacity to generalize to unseen timestamps was also affirmed, highlighting its robustness and adaptability.

Practical and Theoretical Implications

The practical implication of this research is a more precise temporal reasoning within KGs, vital for domains such as historical data analysis, social networks, and event prediction. Theoretically, it bridges the gap between static and temporal representations, offering a pathway for further explorations into time-sensitive machine learning models.

Future Directions

The authors indicate future research could focus on discovering other functions suitable for historical entity evolution modeling, tailored specific to different types of temporal information. Moreover, the application of these embeddings in broader AI contexts, such as natural language processing and temporal event prediction, presents promising avenues for research.

In conclusion, the paper effectively addresses a gap in temporal knowledge representation, proposing a versatile and expressive model suitable for modern temporal datasets. The novel diachronic embedding framework stands to significantly enhance the state of temporal KGC and stimulates further research into time-aware AI applications.