# Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion

arXiv: https://arxiv.org/abs/1611.04125 · HTML: https://mlsanity.com/paper/1611.04125/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2016.

## Built on by (most cited first, 4 of 4)

- [XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge](https://mlsanity.com/paper/2109.12573/) (2021)
- [Multimodal Learning on Graphs for Disease Relation Extraction](https://mlsanity.com/paper/2203.08893/) (2022)
- [Integrating Heterogeneous Domain Information into Relation Extraction: A Case Study on Drug-Drug Interaction Extraction](https://mlsanity.com/paper/2212.10714/) (2022)
- [Joint Representations of Text and Knowledge Graphs for Retrieval and Evaluation](https://mlsanity.com/paper/2302.14785/) (2023)

## Abstract (arXiv)

Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space. In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration. In experiments, we evaluate the joint learning model on three tasks including entity prediction, relation prediction and relation classification from text. The experiment results show that our model can significantly and consistently improve the performance on the three tasks as compared with other baselines.