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The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

The purpose of this paper is to explore the reintegration process of Wounded, Injured or Sick Employees (WISE) of the Dutch Military Armed Forces.

The research method is an exploratory, qualitative case study. A purposive sampling was drawn, including 10 WISE, and 6 reintegration stakeholders. A total of 16 interviews were conducted to explore the individual, organisational and socio-environmental factors that influence reintegration of WISE.

Findings show the importance of involvement and participation of members of the social environment in the reintegration process. Findings show that the complexity of the plethora of WISEs' injuries and disabilities requires a more person-centric reintegration approach with personalized-customized provisions, rather than a policy-driven approach to the reintegration, in order to enhance the reintegration experience and to arrive at beneficial individual and organisational reintegration outcomes.

This cross-sectional study on a limited sample of WISE and reintegration stakeholders does not allow for making inferences about the long-term effects of the reintegration process on reintegration outcomes of the wider population of WISE. Future longitudinal research, encompassing a larger sample, could examine the long-term career, organisational and societal implications of reintegration of WISE within and outside the Military Armed Forces.

This paper presents a “Wounded Warrior Workplace Reintegration Program”, aimed at deriving beneficial outcomes for all stakeholders involved in the reintegration trajectory.

This paper contributes to the literature by presenting a Model of Occupational Reintegration of WISE that considers the factors at an individual, social-environmental, and institutional level as determinants of successful reintegration.