# DEER: Descriptive Knowledge Graph for Explaining Entity Relationships

arXiv: https://arxiv.org/abs/2205.10479 · HTML: https://mlsanity.com/paper/2205.10479/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 16 of 16)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [A Survey of Knowledge-Enhanced Text Generation](https://mlsanity.com/paper/2010.04389/) (2020)
- [KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning](https://mlsanity.com/paper/2009.12677/) (2020)
- [Definition Modeling: Learning to define word embeddings in natural language](https://mlsanity.com/paper/1612.00394/) (2016)
- [Variational Knowledge Graph Reasoning](https://mlsanity.com/paper/1803.06581/) (2018)
- [Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training](https://mlsanity.com/paper/2010.12688/) (2020)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [DualTKB: A Dual Learning Bridge between Text and Knowledge Base](https://mlsanity.com/paper/2010.14660/) (2020)
- [Open Relation Modeling: Learning to Define Relations between Entities](https://mlsanity.com/paper/2108.09241/) (2021)
- [Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web](https://mlsanity.com/paper/2103.15950/) (2021)

## Built on by (most cited first, 7 of 7)

- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [CCGen: Explainable Complementary Concept Generation in E-Commerce](https://mlsanity.com/paper/2305.11480/) (2023)
- [VER: Unifying Verbalizing Entities and Relations](https://mlsanity.com/paper/2211.11093/) (2022)
- [CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions](https://mlsanity.com/paper/2405.00523/) (2024)
- [LMExplainer: Grounding Knowledge and Explaining Language Models](https://mlsanity.com/paper/2303.16537/) (2023)
- [RelBERT: Embedding Relations with Language Models](https://mlsanity.com/paper/2310.00299/) (2023)
- [Descriptive Knowledge Graph in Biomedical Domain](https://mlsanity.com/paper/2310.11681/) (2023)

## Abstract (arXiv)

We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as ``Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.'' To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation.