CoKE: Contextualized Knowledge Graph Embedding (1911.02168v2)

Abstract: Knowledge graph embedding, which projects symbolic entities and relations into continuous vector spaces, is gaining increasing attention. Previous methods allow a single static embedding for each entity or relation, ignoring their intrinsic contextual nature, i.e., entities and relations may appear in different graph contexts, and accordingly, exhibit different properties. This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings. Two types of graph contexts are studied: edges and paths, both formulated as sequences of entities and relations. CoKE takes a sequence as input and uses a Transformer encoder to obtain contextualized representations. These representations are hence naturally adaptive to the input, capturing contextual meanings of entities and relations therein. Evaluation on a wide variety of public benchmarks verifies the superiority of CoKE in link prediction and path query answering. It performs consistently better than, or at least equally well as current state-of-the-art in almost every case, in particular offering an absolute improvement of 21.0% in H@10 on path query answering. Our code is available at \url{https://github.com/PaddlePaddle/Research/tree/master/KG/CoKE}.

• The paper introduces a Transformer-based model that generates context-aware KG embeddings to capture complex entity and relation dynamics.
• It leverages sequential modeling of graph contexts through Transformer blocks to significantly improve link prediction and multi-hop reasoning.
• Experiments on FB15k, WN18, and other benchmarks reveal up to a 21% improvement in H@10 for path query answering with efficient parameter use.

An Overview of CoKE: Contextualized Knowledge Graph Embedding

The paper "CoKE: Contextualized Knowledge Graph Embedding" presents a new paradigm in the domain of knowledge graph embeddings by introducing contextualized representations for entities and relations. The traditional methods in knowledge graph embeddings typically assign a static vector for each entity or relation, which may not accurately reflect the intrinsic contextual variations in different graph scenarios. This paper proposes CoKE (Contextualized Knowledge Graph Embedding), employing a Transformer-based approach to address this limitation, providing dynamic, flexible representations adaptive to their graph context.

Core Contributions

The central innovation in CoKE lies in the employment of a Transformer encoder to process sequences of entities and relations from graph contexts, which include both simple edges and complex multi-hop paths. By leveraging the inherent sequence modeling capability of Transformers, CoKE generates embeddings that are sensitive to the input context, capturing more nuanced meanings.

Methodology

CoKE models entity and relation representations as functions of their respective graph contexts, which are formalized as sequences. The sequences are encoded using a stack of Transformer blocks, producing contextualized representations. Notably, the method accommodates two types of graph contexts: single triples (edges) and multi-relation sequences (paths).

For training, the model utilizes an entity prediction task where an entity is masked within a graph context, and the model must predict the masked entity from the contextually-encoded sequence. This training approach is aligned with the tasks it aims to improve upon: link prediction and path query answering.

Results and Performance

The evaluation includes extensive experiments on multiple established benchmarks: FB15k, WN18, FB15k-237, and WN18RR for link prediction, and additional datasets for path query answering. CoKE consistently outperforms existing models in both domains, achieving superior or competitive results in nearly all metrics of interest. Specifically notable is its outstanding performance in path query answering, offering an absolute improvement of up to 21.0% in H@10 on Freebase paths, highlighting its capability in multi-hop reasoning.

Furthermore, the paper reports CoKE's efficient parameter utilization, providing improved performance with fewer parameters compared to state-of-the-art methods, underscoring its potential for application in resource-constrained environments.

Implications and Future Directions

The implications of CoKE in both theoretical and practical applications are significant. Theoretically, the method delineates a new direction for graph embeddings, suggesting that capturing contextuality could be a key to unlocking richer semantic representations. Practically, its improved performance in tasks like link prediction and path query answering offers immediate utility in areas such as question answering and knowledge discovery.

This work suggests several future research avenues:

1. Generalization to More Complex Graph Contexts: While CoKE mainly explores edges and paths, further extending the model to encompass more complex graph structures could potentially increase its applicability.
2. Broadening Applications: Beyond traditional tasks, CoKE might be adapted to broader domains, including those that require interaction with unstructured data or those in fields like biology where contextuality could yield additional insights.

In conclusion, the introduction of CoKE marks a significant step toward more intelligent, context-aware knowledge graph embedding methodologies, providing both a theoretical basis and practical tools for subsequent research and applications in the field of artificial intelligence.