# Knowledge Graph Representation with Jointly Structural and Textual Encoding

arXiv: https://arxiv.org/abs/1611.08661 · HTML: https://mlsanity.com/paper/1611.08661/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2016.

## Built on by (most cited first, 17 of 17)

- [Exploring Large Language Models for Knowledge Graph Completion](https://mlsanity.com/paper/2308.13916/) (2023)
- [Multi-task Pre-training Language Model for Semantic Network Completion](https://mlsanity.com/paper/2201.04843/) (2022)
- [Graph-Text Multi-Modal Pre-training for Medical Representation Learning](https://mlsanity.com/paper/2203.09994/) (2022)
- [KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks](https://mlsanity.com/paper/2302.11396/) (2023)
- [Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning](https://mlsanity.com/paper/2111.06103/) (2021)
- [Towards Semantically Enriched Embeddings for Knowledge Graph Completion](https://mlsanity.com/paper/2308.00081/) (2023)
- [Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model](https://mlsanity.com/paper/2312.01837/) (2023)
- [Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web](https://mlsanity.com/paper/2103.15950/) (2021)
- [Discovering Supply Chain Links with Augmented Intelligence](https://mlsanity.com/paper/2111.01878/) (2021)
- [TeKo: Text-Rich Graph Neural Networks with External Knowledge](https://mlsanity.com/paper/2206.07253/) (2022)
- [VEM2L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2207.01528/) (2022)
- [Manufacturing Service Capability Prediction with Graph Neural Networks](https://mlsanity.com/paper/2403.17239/) (2024)
- [Knowledge Graph Completion using Structural and Textual Embeddings](https://mlsanity.com/paper/2404.16206/) (2024)
- [KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models](https://mlsanity.com/paper/2506.03576/) (2025)
- [Swarm Differential Privacy for Purpose Driven Data-Information-Knowledge-Wisdom Architecture](https://mlsanity.com/paper/2105.04045/) (2021)
- [Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion](https://mlsanity.com/paper/2204.13097/) (2022)
- [Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs](https://mlsanity.com/paper/2605.05463/) (2026)

## Abstract (arXiv)

The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline by margin on link prediction and triplet classification tasks. Source codes of this paper will be available on Github.