# Discovering Fine-Grained Semantics in Knowledge Graph Relations

arXiv: https://arxiv.org/abs/2202.08917 · HTML: https://mlsanity.com/paper/2202.08917/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [KBQA: Learning Question Answering over QA Corpora and Knowledge Bases](https://mlsanity.com/paper/1903.02419/) (2019)
- [Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space](https://mlsanity.com/paper/1504.06654/) (2015)
- [TransG : A Generative Mixture Model for Knowledge Graph Embedding](https://mlsanity.com/paper/1509.05488/) (2015)
- [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://mlsanity.com/paper/1902.00172/) (2019)
- [Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods](https://mlsanity.com/paper/1504.06658/) (2015)
- [Knowledge Graph Embedding with Multiple Relation Projections](https://mlsanity.com/paper/1801.08641/) (2018)
- [Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space](https://mlsanity.com/paper/1809.03401/) (2018)
- [Entity Type Prediction in Knowledge Graphs using Embeddings](https://mlsanity.com/paper/2004.13702/) (2020)
- [Vector Embedding of Wikipedia Concepts and Entities](https://mlsanity.com/paper/1702.03470/) (2017)

## Built on by (most cited first, 2 of 2)

- [OntoAligner Meets Knowledge Graph Embedding Aligners](https://mlsanity.com/paper/2509.26417/) (2025)
- [RSCF: Relation-Semantics Consistent Filter for Entity Embedding of Knowledge Graph](https://mlsanity.com/paper/2505.20813/) (2025)

## Abstract (arXiv)

When it comes to comprehending and analyzing multi-relational data, the semantics of relations are crucial. Polysemous relations between different types of entities, that represent multiple semantics, are common in real-world relational datasets represented by knowledge graphs. For numerous use cases, such as entity type classification, question answering and knowledge graph completion, the correct semantic interpretation of these relations is necessary. In this work, we provide a strategy for discovering the different semantics associated with abstract relations and deriving many sub-relations with fine-grained meaning. To do this, we leverage the types of the entities associated with the relations and cluster the vector representations of entities and relations. The suggested method is able to automatically discover the best number of sub-relations for a polysemous relation and determine their semantic interpretation, according to our empirical evaluation.