# An Automatic Ontology Generation Framework with An Organizational Perspective

arXiv: https://arxiv.org/abs/2201.05910 · HTML: https://mlsanity.com/paper/2201.05910/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 66 references that are in the map.
- First posted on arXiv 15 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Knowledge Graph Completion via Complex Tensor Factorization](https://mlsanity.com/paper/1702.06879/) (2017)
- [A Semantic Matching Energy Function for Learning with Multi-relational Data](https://mlsanity.com/paper/1301.3485/) (2013)
- [Knowledge Base Completion: Baselines Strike Back](https://mlsanity.com/paper/1705.10744/) (2017)
- [A survey of embedding models of entities and relationships for knowledge graph completion](https://mlsanity.com/paper/1703.08098/) (2017)
- [Which Knowledge Graph Is Best for Me?](https://mlsanity.com/paper/1809.11099/) (2018)
- [Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective](https://mlsanity.com/paper/1903.11406/) (2019)
- [Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data](https://mlsanity.com/paper/1903.04360/) (2019)
- [A Framework for Managing Evolving Information Resources on the Data Web](https://mlsanity.com/paper/1504.06451/) (2015)
- [Completeness and Consistency Analysis for Evolving Knowledge Bases](https://mlsanity.com/paper/1811.12721/) (2018)

## Built on by (most cited first, 6 of 6)

- [Development of Ontological Knowledge Bases by Leveraging Large Language Models](https://mlsanity.com/paper/2601.10436/) (2026)
- [What is Cognitive Computing? An Architecture and State of The Art](https://mlsanity.com/paper/2301.00882/) (2023)
- [Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings](https://mlsanity.com/paper/2308.03519/) (2023)
- [Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information](https://mlsanity.com/paper/2504.07738/) (2025)
- [LLM-based Zero-shot Triple Extraction for Automated Ontology Generation from Software Engineering Standards](https://mlsanity.com/paper/2509.00140/) (2025)
- [COntExt: Towards Context-Aware Ontology Extension from Operational Metrics](https://mlsanity.com/paper/2607.29553/) (2026)

## Abstract (arXiv)

Ontologies have been known for their semantic representation of knowledge. ontologies cannot automatically evolve to reflect updates that occur in respective domains. To address this limitation, researchers have called for automatic ontology generation from unstructured text corpus. Unfortunately, systems that aim to generate ontologies from unstructured text corpus are domain-specific and require manual intervention. In addition, they suffer from uncertainty in creating concept linkages and difficulty in finding axioms for the same concept. Knowledge Graphs (KGs) has emerged as a powerful model for the dynamic representation of knowledge. However, KGs have many quality limitations and need extensive refinement. This research aims to develop a novel domain-independent automatic ontology generation framework that converts unstructured text corpus into domain consistent ontological form. The framework generates KGs from unstructured text corpus as well as refine and correct them to be consistent with domain ontologies. The power of the proposed automatically generated ontology is that it integrates the dynamic features of KGs and the quality features of ontologies.