# Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

arXiv: https://arxiv.org/abs/2204.13097 · HTML: https://mlsanity.com/paper/2204.13097/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 63 references that are in the map.
- First posted on arXiv 27 Apr 2022.

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

- [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)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Knowledge Graph Embedding for Link Prediction: A Comparative Analysis](https://mlsanity.com/paper/2002.00819/) (2020)
- [Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction](https://mlsanity.com/paper/1307.7973/) (2013)
- [Knowledge Graph Representation with Jointly Structural and Textual Encoding](https://mlsanity.com/paper/1611.08661/) (2016)
- [Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks](https://mlsanity.com/paper/1704.08384/) (2017)
- [Edge: Enriching Knowledge Graph Embeddings with External Text](https://mlsanity.com/paper/2104.04909/) (2021)
- [pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference](https://mlsanity.com/paper/1810.08854/) (2018)
- [Interpretable Question Answering on Knowledge Bases and Text](https://mlsanity.com/paper/1906.10924/) (2019)
- [Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space](https://mlsanity.com/paper/1809.03401/) (2018)
- [Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning](https://mlsanity.com/paper/2004.08798/) (2020)
- [Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data](https://mlsanity.com/paper/1605.05416/) (2016)
- [RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding](https://mlsanity.com/paper/2101.10070/) (2021)

## Abstract (arXiv)

Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textual mentions of entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between two entities. However, it is not possible to represent relations between entities that do not co-occur in a single sentence using LDPs. In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i.e. with mention entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i.e. without mention entity pairs). We propose a supervised borrowing method, SuperBorrow, that learns to score the suitability of an LDP to represent a without-mention entity pair using pre-trained entity embeddings and contextualised LDP representations. Experimental results show that SuperBorrow improves the link prediction performance of multiple widely-used prior KGE methods such as TransE, DistMult, ComplEx and RotatE.