# Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs

arXiv: https://arxiv.org/abs/1808.07724 · HTML: https://mlsanity.com/paper/1808.07724/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2018.

## Built on by (most cited first, 14 of 14)

- [A Decade of Knowledge Graphs in Natural Language Processing: A Survey](https://mlsanity.com/paper/2210.00105/) (2022)
- [Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation](https://mlsanity.com/paper/2212.11868/) (2022)
- [Edge: Enriching Knowledge Graph Embeddings with External Text](https://mlsanity.com/paper/2104.04909/) (2021)
- [A Unified Model for Reverse Dictionary and Definition Modelling](https://mlsanity.com/paper/2205.04602/) (2022)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [PREDICT: Persian Reverse Dictionary](https://mlsanity.com/paper/2105.00309/) (2021)
- [Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest](https://mlsanity.com/paper/2312.12989/) (2023)
- [Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification](https://mlsanity.com/paper/2405.19967/) (2024)
- [Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation](https://mlsanity.com/paper/2307.15776/) (2023)
- [Improving Narrative Relationship Embeddings by Training with Additional Inverse-Relationship Constraints](https://mlsanity.com/paper/2212.11234/) (2022)
- [Towards a Transformer-Based Reverse Dictionary Model for Quality Estimation of Definitions](https://mlsanity.com/paper/2311.02985/) (2023)
- [GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse Dictionary](https://mlsanity.com/paper/2412.06654/) (2024)
- [Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2602.12419/) (2026)
- [Aligning Biomedical Texts and Knowledge Graphs: A Systematic Comparison of Lightweight Alignment Strategies](https://mlsanity.com/paper/2608.23214/) (2026)

## Abstract (arXiv)

This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambiguation mechanism on the input word embeddings (a Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities. The ideas of this work are demonstrated on large-scale text-to-entity mapping and entity classification tasks, with state of the art results.