Abstract: The accelerating pace of digital transformation is reshaping labour-market dynamics, driving the emergence of new competencies, and intensifying the need for scalable skill-intelligence systems within open innovation ecosystems. Yet, research on Indonesian Named Entity Recognition (NER) remains limited for skill-extraction tasks, especially in low-resource contexts where annotated data are scarce and novel skill expressions evolve rapidly. To address this gap, this study contributes to applied Natural Language Processing (NLP) by introducing the Few-Shot Semantic Meta-Learning framework with CRF (FSM-CRF) for Indonesian skill entity recognition, which integrates semantic span representations, episodic meta-learning, and BIO-constrained CRF decoding to enhance prototype stability and entity-boundary precision for complex, multi-token skill expressions. Using the NERSkill.id dataset, the proposed model is evaluated under a 3-way, 10-shot episodic setting and achieves a micro-F1 of 73.84%, outperforming traditional supervised approaches (IndoBERT fine-tuning, BiLSTM-CRF) and existing few-shot baselines. Ablation experiments further demonstrate that semantic span modelling and structured CRF inference play pivotal roles in improving robustness, while meta-learning strengthens adaptability across diverse and evolving skill categories. From an open innovation perspective, this framework offers a data-efficient solution for dynamic competency mapping, reducing dependence on costly annotation pipelines and enabling continuous updates to workforce skill taxonomies. Overall, the findings highlight semantic meta-learning as a promising foundation for next-generation skill-intelligence infrastructures that support AI-enabled innovation management, strategic workforce planning, and evidence-informed policy design.

Nurchim , Muljono, Edi Noersasongko, Ahmad Zainul Fanani and Deshinta Arrova Dewi. “A Few-Shot Semantic Meta-Learning Framework with CRF for Skill Entity Recognition in Open Innovation Ecosystems”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170141

@article{2026,
title = {A Few-Shot Semantic Meta-Learning Framework with CRF for Skill Entity Recognition in Open Innovation Ecosystems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170141},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170141},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Nurchim and Muljono and Edi Noersasongko and Ahmad Zainul Fanani and Deshinta Arrova Dewi}
}