---
title: "多元知识图谱链接预测研究综述 — 隗继耀 2026"
description: "Many real-world facts involve more than two entities."
authors: ["隗继耀", "官赛萍", "靳小龙", "郭嘉丰", "程学旗"]
year: 2026
venue: "中文信息学报 (Journal of Chinese Information Processing)"
source: "http://jcip.cipsc.org.cn/article/doi/10.3969/j.issn.1003-0077.2026.04.001"
doi: "10.3969/j.issn.1003-0077.2026.04.001"
licence: "Free access (jcip.cipsc.org.cn), no open licence stated — readonly; © 中文信息学报"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/wei-2026-link-prediction-over/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/wei-2026-link-prediction-over/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2026](https://md.research.elamur.ai/free/papers/2026/page/5/) / 多元知识图谱链接预测研究综述

# 多元知识图谱链接预测研究综述

**Authors:** 隗继耀, 官赛萍, 靳小龙, 郭嘉丰, 程学旗 · **Year:** 2026 · **Venue:** 中文信息学报 (Journal of Chinese Information Processing) · **Licence:** Free access (jcip.cipsc.org.cn), no open licence stated — readonly; © 中文信息学报

**Links:** [Publisher's page](https://doi.org/10.3969/j.issn.1003-0077.2026.04.001) · [PDF](http://jcip.cipsc.org.cn/cn/article/pdf/preview/10.3969/j.issn.1003-0077.2026.04.001.pdf) · [DOI 10.3969/j.issn.1003-0077.2026.04.001](https://doi.org/10.3969/j.issn.1003-0077.2026.04.001)

## Summary
Many real-world facts involve more than two entities. One example is a person who studied a subject at a university and earned a degree there. Splitting such facts into triples through auxiliary nodes, such as Freebase's CVT entities, loses structural information, adds parameters and makes reasoning harder. N-ary knowledge graphs (NKGs) store these facts directly, but like ordinary KGs they are incomplete. This survey presents itself as the first systematic review of NKG link prediction, the task of recovering missing elements of n-ary facts. It sorts nearly 50 published models along three axes: modeling strategy, fact representation and application setting. For each family, the authors explain the design idea, its strengths and its weaknesses. They also compile benchmark datasets, compare reported results of leading models, review applications in biomedicine and product recommendation, and outline future research directions. The paper gives a structured overview of a fast-growing area that had no dedicated survey before.

## Key points
- Motivation statistics from Freebase: more than a third of entities take part in n-ary facts, and over 61% of fact types are n-ary.
- The surveyed methods fall into three modeling families:
  - **Space-embedding**: Euclidean (m-TransH, the first NKG link prediction model, and RAE), hyperbolic (e.g., HYPER2, PolygonE) and complex-space (ShrinkE, HYPERMONO).
  - **Tensor decomposition**: e.g., GETD, HypE, S2S.
  - **Neural networks**: FCN (NaLP, NeuInfer), CNN (HINGE, HyConvE, HJE), Transformer (GRAN, HyNT, NYLON) and GNN (StarE, HAHE, HyperFormer). Neural models form the most active line of work.
- Three fact formats are compared: hyperedge, unordered role–value pairs, and hyper-relational (a main triple plus qualifiers). The hyper-relational format is the most widely adopted because it stays compatible with ordinary triples.
- Besides the general static setting, the survey covers specialized scenarios: temporal (NE-NET, HypeTKG), few-shot (HANCL, MetaRH) and inductive (QBLP, MetaNIR).
- The authors propose three criteria for choosing NKG modeling over plain triples: participation of several entities, tight semantic coupling between them, and dependence on context such as time or place.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Natural language processing](https://md.research.elamur.ai/free/shelves/nlp/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{2026,
  title = {{多元知识图谱链接预测研究综述}},
  author = {隗继耀 and 官赛萍 and 靳小龙 and 郭嘉丰 and 程学旗},
  journal = {中文信息学报 (Journal of Chinese Information Processing)},
  year = {2026},
  doi = {10.3969/j.issn.1003-0077.2026.04.001},
  url = {https://doi.org/10.3969/j.issn.1003-0077.2026.04.001}
}
```

- [← previous 基于预训练语言模型的汉语字形谜解谜模型](https://md.research.elamur.ai/free/papers/wang-2026-chinese-character-riddle/)
- [next → 大模型参数高效微调方法综述: 技术、趋势与挑战](https://md.research.elamur.ai/free/papers/tang-2026-parameter-efficient-fine/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/papers/2026/page/5/)