Knowledge Graph Embedding with Iterative Guidance from Soft Rules (1711.11231v1)

Abstract: Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various confidence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels. The code and data used for this paper can be obtained from https://github.com/iieir-km/RUGE.

An Overview of "Knowledge Graph Embedding with Iterative Guidance from Soft Rules"

The paper "Knowledge Graph Embedding with Iterative Guidance from Soft Rules" presents an innovative framework for embedding knowledge graphs (KGs) that leverages soft logic rules in an iterative process. The authors, Shu Guo, Quan Wang, Lihong Wang, Bin Wang, and Li Guo, propose the Rule-Guided Embedding (RUGE) model, which addresses limitations in previous methods that utilized logical rules, specifically those focusing solely on hard rules and neglecting the iterative potential between embedding learning and logic-based inference.

Core Concept and Methodology

Knowledge graphs, which encode relationships between entities in the form of triples, are essential for various applications in artificial intelligence. The challenge in efficiently utilizing KGs arises from their symbolic representation, making manipulation and semantic understanding difficult. Knowledge graph embedding aims to embed these graphs into a continuous vector space, thus simplifying their manipulation while preserving inherent relational structures.

RUGE introduces a novel approach wherein an embedding model learns from three sources simultaneously:

1. Labeled triples directly observed in the KG.
2. Unlabeled triples whose labels can be predicted iteratively.
3. Soft rules with varying degrees of confidence, extracted automatically from the KG.

The iterative nature of RUGE is key to its enhanced performance. During each iteration, the model involves two alternating stages:

• Soft Label Prediction: This stage predicts soft labels for unlabeled triples using the current embeddings and soft rules. The prediction respects rule-based constraints, optimizing a combination of factors to align soft labels with rule-derived expectations.
• Embedding Rectification: The model integrates the labeled and newly labeled triples to refine current embeddings, ensuring that rule knowledge is effectively transferred into the learned representations.

Experimental Evaluation and Results

The authors rigorously evaluate RUGE on the Freebase (FB15K) and YAGO (YAGO37) datasets, two large and complex knowledge graphs. They perform link prediction tasks, aiming to predict missing entities in triples, a standard benchmarking task for models in this domain.

The results demonstrate that RUGE significantly outperforms state-of-the-art embedding approaches, including those integrating hard logic rules or relation paths in a non-iterative fashion. Notably, the iterative injection of rule knowledge allowed RUGE to achieve consistently superior performance metrics such as MRR and HITS@N on both datasets.

Implications and Future Directions

The contributions of RUGE are threefold. Firstly, it establishes a new paradigm for KG embedding by iteratively incorporating logic rules, demonstrating significant empirical improvements. Secondly, it highlights the value of soft rules, challenging the traditional reliance on hard rules that require substantial manual effort for creation and validation. Thirdly, the framework's flexibility allows it to be adapted for various embedding models and types of logical rules.

With the promise shown by RUGE, further research could explore the integration of even more nuanced rule types, extension to other forms of embedded representations, or the application of the RUGE framework to tasks beyond link prediction. Moreover, exploring more efficient algorithms for rule extraction and propositionalization could enhance scalability for larger knowledge graphs.

In conclusion, "Knowledge Graph Embedding with Iterative Guidance from Soft Rules" presents a methodologically sound approach that advances the field of knowledge graph embeddings by successfully marrying embedding techniques with iterative, soft rule-based logical inferences. The implications for both the academic community and practical applications are significant, as they open avenues for more robust and semantically aware AI systems.