•  
  •  
 

Malaysian Journal of Computing (MJoC)

Corresponding Author

Noor Hasimah Ibrahim Teo ([email protected])

Abstract

This conceptual study proposes a three-layer methodological framework for representing Malay ontological knowledge in continuous vector spaces and integrating ontology-derived representations with machine-learning models. The framework comprises an Ontology Layer, an Embedding Layer, and an Integration Layer. Its contribution is not a new ontology-embedding algorithm rather, it provides a Malay-oriented methodological organization that links ontology construction, embedding-method selection, symbolic-subsymbolic fusion, ontology-guided consistency checking, and downstream evaluation. In the present study, the ontology layer is represented through a prototype Malay linguistic ontology and its class hierarchy and knowledge graph, whereas the embedding, integration, consistency-checking, and downstream evaluation stages are proposed components for subsequent empirical validation. Existing approaches including RDF2Vec, OPA2Vec, OWL2Vec*, and EL Embeddings are used as methodological reference points because they differ in graph representation, lexical information, and preservation of logical semantics. The framework is intended to support future Malay NLP experiments in which ontology-enhanced models can be compared with equivalent models without ontology-derived features. Accordingly, claims concerning accuracy, explainability, semantic disambiguation, and model improvement are treated as hypotheses for future empirical assessment rather than demonstrated outcomes.

Publication Date

10-1-2026

Volume

11

Issue

2

Recommendation of Reviewers

yes

Download

Share

COinS