Ontology Completion with Graph-Based Machine Learning: A Comprehensive Evaluation

Sebastian Mežnar, Matej Bevec, Nada Lavrač, Blaž Škrlj

Machine Learning and Knowledge Extraction 2022 article cited by 10

Source Open-access PDF More in Software & Coding

importance 4/10 partially solved Semantic Web and Ontologies knowledge engineering stage: research

Problem

Growing knowledge bases accumulate wrong or missing entries, and manually finding spurious parts or missing links in ontologies is hard.

Why it matters

Errors in shared knowledge bases propagate into every downstream application that relies on them.

What the AI did

Transformed ontologies into graphs and used structure-only link analysis to recommend missing edges, systematically evaluating thirteen methods across eight semantic resources.

Results

Structure-only link analysis demonstrated to be scalable and computationally efficient for a subset of the analyzed datasets; no numeric metrics stated.

Impact

Authors claim the most extensive systematic study of link analysis methods across semantic resources to date.

Record

Venue	Machine Learning and Knowledge Extraction
Published	2022
Work type	article
Authors	Sebastian Mežnar, Matej Bevec, Nada Lavrač, Blaž Škrlj
Citations	10 (OpenAlex, at import time)
References	59
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/make4040056
Open access	gold
Keywords	ontology completion, link analysis, knowledge graphs, machine learning, semantic resources

Importance rating

4 / 10

real but narrower advance

Knowledge base errors propagate downstream, but abstract only demonstrates scalable link analysis on eight ontologies; narrow, partially-solved advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

More in Software & Coding

#	Paper	Year	Citations	Importance
1	ImageNet classification with deep convolutional neural networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton	2017	106,345	8
2	Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun	2016	221,134	8
3	Faster sorting algorithms discovered using deep reinforcement learning Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelmi, Marco S...	2023	175	8
4	A deep learning‐based approach for software vulnerability detection using code metrics Fazli Subhan, Xiaoxue Wu, Lili Bo, Xiaobing Sun, Muhammad Rahman	2022	42	7
5	Deep Learning-Based Attack Detection and Classification in Android Devices Alfonso Gómez, Antonio Muñoz	2023	41	7