# Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain

arXiv: https://arxiv.org/abs/2011.01103 · HTML: https://mlsanity.com/paper/2011.01103/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis](https://mlsanity.com/paper/2301.08826/) (2023)
- [A Survey on Visual Transfer Learning using Knowledge Graphs](https://mlsanity.com/paper/2201.11794/) (2022)
- [KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration](https://mlsanity.com/paper/2602.20135/) (2026)
- [COMBO: A Complete Benchmark for Open KG Canonicalization](https://mlsanity.com/paper/2302.03905/) (2023)
- [A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models](https://mlsanity.com/paper/2408.02377/) (2024)
- [SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs](https://mlsanity.com/paper/2306.04758/) (2023)
- [Taxonomy Enrichment with Text and Graph Vector Representations](https://mlsanity.com/paper/2201.08598/) (2022)
- [Extracting Blockchain Concepts from Text](https://mlsanity.com/paper/2305.10408/) (2023)
- [Tripl`etoile: Extraction of Knowledge from Microblogging Text](https://mlsanity.com/paper/2408.14908/) (2024)
- [From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics](https://mlsanity.com/paper/2607.21327/) (2026)

## Abstract (arXiv)

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.