---
title: "知识图谱嵌入技术研究综述 — 张天成 2021"
description: "This Chinese-language survey covers knowledge graph embedding (KGE), also called knowledge representation learning."
authors: ["张天成", "田雪", "孙相会", "于明鹤", "孙艳红", "于戈"]
year: 2021
venue: "软件学报 (Journal of Software)"
source: "https://jos.org.cn/jos/article/abstract/6429"
doi: "10.13328/j.cnki.jos.006429"
licence: "Free access (jos.org.cn), no open licence stated — readonly; © 中国科学院软件研究所 / 软件学报"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/zhang-2021-overview-knowledge-graph/"
---
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# 知识图谱嵌入技术研究综述

**Authors:** 张天成, 田雪, 孙相会, 于明鹤, 孙艳红, 于戈 · **Year:** 2021 · **Venue:** 软件学报 (Journal of Software) · **Licence:** Free access (jos.org.cn), no open licence stated — readonly; © 中国科学院软件研究所 / 软件学报

**Links:** [Publisher's page](https://doi.org/10.13328/j.cnki.jos.006429) · [PDF](https://www.jos.org.cn//jos/article/pdf/6429) · [DOI 10.13328/j.cnki.jos.006429](https://doi.org/10.13328/j.cnki.jos.006429)

## Summary
This Chinese-language survey covers knowledge graph embedding (KGE), also called knowledge representation learning. KGE maps the entities and relations of a knowledge graph into dense, low-dimensional vectors. The motivation is that symbolic (head, relation, tail) triples are hard to compute with, while learned vectors keep the graph's structure and make similarity explicit. The authors extend the earlier survey by Wang et al. and organise the field in three parts:
- embeddings learned only from observed facts, split into distance-based and semantic-matching models;
- dynamic, time-aware KGE;
- KGE that adds extra information such as entity types, textual descriptions, relation paths and logical rules.

Models are compared by how they represent entities and relations and by their scoring functions. A recurring theme is how translation models deal with complex relations, where TransE fails. The paper also reviews downstream uses (question answering, recommendation, relation extraction) and outlines open challenges and future directions. It is a structured overview rather than an experimental study, and it is useful as an entry map of the KGE model landscape.

## Key points
- Bibliographic detail: the PDF header gives Journal of Software 2023, 34(1): 277–311. The paper was received in 2021 and published online on 2021-10-20, which explains the "2021" citation.
- Example KGs named in the paper: Freebase, DBpedia, YAGO, NELL and Wikidata. The notation is G = (E, R, S), with facts written as (h, r, t).
- Scoring functions fall into two families:
  - distance-based, illustrated by TransE and its h + r ≈ t principle;
  - similarity-based, illustrated by DistMult.
  - SE and UM serve as early baselines.
- Relations are typed as 1-1, 1-N, N-1 or N-N. The last three count as "complex" and motivate TransE extensions: TransH, TransR, TransD, STransE, TranSparse (share/separate), TransM, TransA, TransF, ITransF, TransAt and TransMS.
- Representative dynamic KGE methods discussed: t-TransE, Know-Evolve, HyTE and TDG2E.

*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{2021,
  title = {{知识图谱嵌入技术研究综述}},
  author = {张天成 and 田雪 and 孙相会 and 于明鹤 and 孙艳红 and 于戈},
  journal = {软件学报 (Journal of Software)},
  year = {2021},
  doi = {10.13328/j.cnki.jos.006429},
  url = {https://doi.org/10.13328/j.cnki.jos.006429}
}
```

- [← previous Zero-Shot Text-to-Image Generation](https://md.research.elamur.ai/free/papers/ramesh-2021-zero-shot-text-image-generation/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/papers/2021/page/4/)

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