# Enterprise Use Cases Combining Knowledge Graphs and Natural Language Processing

arXiv: https://arxiv.org/abs/2404.01443 · HTML: https://mlsanity.com/paper/2404.01443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 65 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Text Generation from Knowledge Graphs with Graph Transformers](https://mlsanity.com/paper/1904.02342/) (2019)
- [KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering](https://mlsanity.com/paper/2110.04330/) (2021)
- [Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://mlsanity.com/paper/1903.01306/) (2019)
- [Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment](https://mlsanity.com/paper/1806.06478/) (2018)
- [A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges](https://mlsanity.com/paper/2007.13069/) (2020)
- [A Decade of Knowledge Graphs in Natural Language Processing: A Survey](https://mlsanity.com/paper/2210.00105/) (2022)
- [A Web-scale system for scientific knowledge exploration](https://mlsanity.com/paper/1805.12216/) (2018)
- [Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches](https://mlsanity.com/paper/2211.16285/) (2022)
- [Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs](https://mlsanity.com/paper/1910.08435/) (2019)
- [PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2210.05245/) (2022)
- [Stage-wise Fine-tuning for Graph-to-Text Generation](https://mlsanity.com/paper/2105.08021/) (2021)
- [A System for Automated Open-Source Threat Intelligence Gathering and Management](https://mlsanity.com/paper/2101.07769/) (2021)
- [Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics](https://mlsanity.com/paper/2210.06023/) (2022)
- [PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention](https://mlsanity.com/paper/2107.04880/) (2021)
- [Semantic Similarity-Based Clustering of Findings From Security Testing Tools](https://mlsanity.com/paper/2211.11057/) (2022)

## Abstract (arXiv)

Knowledge management is a critical challenge for enterprises in today's digital world, as the volume and complexity of data being generated and collected continue to grow incessantly. Knowledge graphs (KG) emerged as a promising solution to this problem by providing a flexible, scalable, and semantically rich way to organize and make sense of data. This paper builds upon a recent survey of the research literature on combining KGs and Natural Language Processing (NLP). Based on selected application scenarios from enterprise context, we discuss synergies that result from such a combination. We cover various approaches from the three core areas of KG construction, reasoning as well as KG-based NLP tasks. In addition to explaining innovative enterprise use cases, we assess their maturity in terms of practical applicability and conclude with an outlook on emergent application areas for the future.