Integration of Ontology Transformation into Hidden Markov Model
Journal Title: Information Dynamics and Applications - Year 2022, Vol 1, Issue 1
Abstract
The goal of this study is to suggest a method for turning an ontology into a hidden Markov model (HMM). Ontology properties (relationships between classes) and ontology classes are taken as HMM symbols and states, respectively. Knowledge is represented in many different fields using the central element of the Semantic Web dubbed ontology. The authors employed machine learning technologies like HMM to add knowledge to these ontologies or to extract knowledge from within them. The meaning obtained from ontologies is not described during this task. The ontology triples that were extracted using SPARQL queries are used in this paper to transform the ontology into an HMM in order to handle this semantic. The Pizza ontology has been used to implement this method, which is based on lightweight ontologies.
Authors and Affiliations
Lazarre Warda*, Guidedi Kaladzavi, Amaria Samdalle, Kolyang
Keywords
Related Articles
- EP ID EP732282
- DOI https://doi.org/10.56578/ida010102
- Views 80
- Downloads 0
Advancements in Image Recognition: A Siamese Network Approach
In the realm of computer vision, image recognition serves as a pivotal task with extensive applications in intelligent security, autonomous driving, and robotics. Traditional methodologies for image recognition often gra...
A Cervical Lesion Recognition Method Based on ShuffleNetV2-CA
Cervical cancer is the second most common cancer among women globally. Colposcopy plays a vital role in assessing cervical intraepithelial neoplasia (CIN) and screening for cervical cancer. However, existing colposcopy m...
DV-Hop Positioning Method Based on Multi-Strategy Improved Sparrow Search Algorithm
In order to address the problem of large positioning errors in non-ranging positioning algorithms for wireless sensor networks (WSN), this study proposes a Distance Vector-Hop (DV-Hop) positioning method based on the m...
Cryptocurrency Investigations in Digital Forensics: Contemporary Challenges and Methodological Advances
Digital forensics, a crucial subset of cybersecurity, encompasses sophisticated tools and methodologies for the interpretation, analysis, and investigation of digital evidence, facilitating the identification and mitigat...
Optimizing Software Vulnerability Detection with MDSADNet: A Multi-Scale Convolutional Approach Enhanced by Mantis-Inspired Optimization
The persistent emergence of software vulnerabilities necessitates the development of effective detection methodologies. Machine learning (ML) and deep learning (DL) offer promising avenues for automating feature extracti...