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Paper: https://dl.acm.org/doi/10.1145/3690624.3709195

Code: Github Repo

Dataset: Zenodo page

Weights & Biases Report: here

Blog Author: Gongzhu Yin

Blog Chinese Version: N元知识图谱上的归纳式关系推理： 一种基于语义超图的推理方法

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Highlights: 🧐Explore Inductive Reasoning on N-ary Knowledge Graphs

This work systematically explore inductive relation reasoning in the context of n-ary knowledge graphs—a more realistic yet underexplored setting. We define two core tasks:

• Transfer reasoning (with/without entity features), mimicking real-world scenarios like emerging entities or evolving knowledge graphs;

• Pairwise subgraph reasoning, where the goal is to infer the likelihood of a hidden relation between two entities based on their local subgraphs—e.g., predicting implicit partnerships between companies.

  Our study reveals that existing approaches—whether designed for binary graphs or directly extended to n-ary settings—fall short in these inductive tasks.

🤗Introducing the N-ary Semantic Hypergraph and Subgraph Sampling Process

We extend traditional hypergraph structures by proposing the n-ary semantic hypergraph, where each fact is represented as a hyperedge with role-aware semantics for every entity. This structure offers several distinct advantages for fully inductive scenarios:

• Flexible multi-hop neighborhood expansion from any participating entity;
• High expressive power without needing to distinguish primary from auxiliary roles;
• Seamless compatibility with existing hypergraph neural networks (HGNNs).

🌐A Subgraph Learning Framework and the NS-HART Aggregation Network

We introduce a subgraph learning framework tailored to this new structure, and design NS-HART, a novel aggregation network for inductive reasoning over n-ary semantic hypergraphs. NS-HART enhances the dual-phase message passing in HGNNs (node → hyperedge, hyperedge → node) by incorporating role-aware Transformer aggregators that explicitly model intra- and inter-fact semantic interactions across multiple hops.

From an optimization perspective, we show that in this learning framework, the expressive power for inductive reasoning lies within the aggregation network itself, rather than in shallow entity or relation embeddings.

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From Binary Relations to N-ary Relations

1. Limitations of Binary Relations

Imagine you want to describe the fact: “Companies A, B, and C are collaborating on Project AIP.” Using traditional knowledge graphs (triplets), you might split this into multiple binary relations: (Company A, participates in, AIP), (Company B, participates in, AIP), (Company C, participates, AIP), (Company A, cooperates, Company B), (Company B, cooperates, Company C), and (Company A, cooperates, Company C). However, it cannot depict the holistic nature of collaboration—this is a joint activity among A, B, and C as a group, not merely isolated bilateral engagements.

When these triples are mixed with thousands of others, it becomes increasingly hard to tell whether A, B, and C participated independently or acted as a collective.

This is where n-ary relations come in. They allow us to represent complex, multi-entity associations in a single fact, preserving semantic cohesion and contextual integrity—especially vital when modeling events and complex interactions in organizations. Let’s look at some real-world examples:

• Drug-Disease-Treatment (Medical Knowledge Graphs):
  • N-ary relation: treats(drug, disease, dosage, duration)
  • Information Loss: Breaking it down loses critical therapeutic effectiveness tied to specific dosage-duration combinations.
• Scientific Experiments (Experimental Variables):
  • N-ary Relation: experiment(method, material, temperature, pressure, result)
  • Information Loss: Splitting loses coupling conditions, making it impossible to capture precise experimental outcomes tied to specific conditions.
• Event Prediction (Spatiotemporal Knowledge Graphs):
  • N-ary Relation: event(subject, action, object, time, location)
  • Information Loss: Splitting fails to convey complete event semantics like specific transactions by a suspect at particular times and places.

While some triple decompositions may preserve surface-level information, they break the semantic bonds between entities, increasing ambiguity and weakening downstream reasoning. This often results in ambiguous semantic connections and noisier reasoning chains.

2. Existing Representations of N-ary Relations

![VD~XMT2SV601E6}]_SQ}$17.png](attachment:06b08783-520b-4107-87ac-e83ab8b8b44e:VDXMT2SV601E6_SQ17.png)

Two main representations exist: