Transformers for Complex Query Answering over Knowledge Hypergraphs (2504.16537v1)

Abstract: Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modalities have been introduced. Triple KGs, as the classic KGs composed of entities and relations of arity 2, have limited representation of real-world facts. Real-world data is more sophisticated. While hyper-relational graphs have been introduced, there are limitations in representing relationships of varying arity that contain entities with equal contributions. To address this gap, we sampled new CQA datasets: JF17k-HCQA and M-FB15k-HCQA. Each dataset contains various query types that include logical operations such as projection, negation, conjunction, and disjunction. In order to answer knowledge hypergraph (KHG) existential first-order queries, we propose a two-stage transformer model, the Logical Knowledge Hypergraph Transformer (LKHGT), which consists of a Projection Encoder for atomic projection and a Logical Encoder for complex logical operations. Both encoders are equipped with Type Aware Bias (TAB) for capturing token interactions. Experimental results on CQA datasets show that LKHGT is a state-of-the-art CQA method over KHG and is able to generalize to out-of-distribution query types.

• The paper introduces the Logical Knowledge Hypergraph Transformer (LKHGT), a model that extends traditional knowledge graphs to hypergraphs and uses transformer components (Projection Encoder, Logical Encoder with Type Aware Bias) to handle n-ary relations and complex logical queries.
• Experimental results on new datasets (JF17k-HCQA, M-FB15k-HCQA) show that LKHGT significantly outperforms existing models in answering complex queries over hypergraphs, demonstrating strong generalization capabilities.
• Key innovations include the use of Type Aware Bias in the transformer architecture and replacing fuzzy logic with transformer mechanisms for logical operations, advancing neural-symbolic reasoning and enabling more realistic AI models for complex data.

Essay: "Transformers for Complex Query Answering over Knowledge Hypergraphs"

The paper titled "Transformers for Complex Query Answering over Knowledge Hypergraphs" presents a novel approach in addressing the Complex Query Answering (CQA) task by extending traditional knowledge graphs to hypergraphs and utilizing transformer-based models for effective reasoning. This paper primarily explores the limitations of classic knowledge graphs, which are largely confined to binary relations and proposes a solution through knowledge hypergraphs, enabling representation and inference of n-ary relations.

Key Contributions

The paper introduces two significant datasets, JF17k-HCQA and M-FB15k-HCQA, crafted to test the representation and reasoning capabilities of knowledge hypergraphs in answering complex queries involving logical operations such as projection, negation, conjunction, and disjunction. The datasets are developed to fill the gap in current CQA models, which predominantly focus on binary relations while disregarding the intricacies of real-world relational data that often require n-ary facts.

At the core of the proposed solution is the Logical Knowledge Hypergraph Transformer (LKHGT). This model is composed of two key components:

1. Projection Encoder: Responsible for managing atomic facet computations involving a hyperedge, where a hyperedge can represent relations of varying arity.
2. Logical Encoder: Focuses on processing complex logical operations beyond simple projections, effectively handling conjunctions, disjunctions, and negations through a refined self-attention mechanism enhanced by Type Aware Bias (TAB).

Evaluation and Results

Experimental validation on introduced datasets reveals that LKHGT consistently outperforms existing CQA models tailored for hyper-relational graphs, notably showcasing superior performance in handling n-ary queries and complex logical reasoning tasks. The model demonstrates robust generalization capabilities, effectively answering out-of-distribution queries which existing models traditionally struggle with.

Moreover, the paper employs baselines including NQE, LSGT, and HLMPNN, facilitating comparative analysis. LKHGT exhibits notable enhancement over these models, especially in tasks involving higher-order logic such as 3-hop intersections.

Analysis of Innovation

The innovative employment of Type Aware Bias within transformer encoders represents a significant departure from traditional transformer models which treat all tokens homogeneously. TAB introduces inductive bias considering token interactions, optimizing self-attention mechanisms to grasp intricate interaction patterns inherent in knowledge hypergraphs.

Additionally, replacing fuzzy logic techniques with transformer mechanisms for logical operations positions LKHGT as a leap forward in neural-symbolic reasoning, bridging gaps observed in previous methods and paving the path for enhancements in logic-based AI applications.

Implications and Future Directions

The implications of this research are profound both theoretically and practically. By extending the scope of query answering from binary to n-ary relations, it lays a foundation for more realistic and applicable AI models capable of interacting with complex datasets representative of real-world scenarios.

Future developments might involve exploring more sophisticated biases and expanding upon the sequential processing in neural architectures. Efforts might also focus on optimizing computational efficiency and scalability given the observed time complexity with iterative query processing, ensuring applicability across diverse and sizable datasets.

In summary, the research not only advances CQA methodologies but also solidifies the utilization of transformers in understanding and operating on complex data structures like hypergraphs, embodying a promising step toward intelligent reasoning frameworks adept at handling multi-faceted queries in knowledge-rich environments. The continued exploration and refinement of such systems could profoundly impact AI capabilities, allowing intelligent systems to infer, reason, and act upon information with unprecedented depth and accuracy.