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Ontology-augmented health recommendation

Paper · course project · 2026 · Key work

Ontology-augmented personal healthcare service recommendation is a course project for an ontology-engineering class at Chiang Mai University. It models healthcare in Thailand (diseases, symptoms, treatments, specialties, facilities and insurance) as an OWL 2 DL ontology, so a reasoner can recommend where a patient should go and what their insurance covers.

The ontology

The Thai Healthcare Service Ontology is written in OWL 2 DL and published in OWL, Turtle and JSON-LD. Its README describes:

  • 85 classes covering diseases, symptoms, treatments and medical specialties;
  • real Thai hospitals and clinics with location data, among 244 individuals;
  • insurance: providers, coverage plans and cost calculation, including the national Gold Card scheme;
  • Thai and English labels for every entity;
  • 38 SWRL rules rules for detecting emergencies, recommending specialties and working out coverage.

Working with it

The ontology is loaded and queried from Python with Owlready2, with HermiT or Pellet as optional reasoners. A set of SPARQL queries answers questions about insurance and services. Validation scripts check the ontology's structure and run its competency questions: the questions an ontology must be able to answer, written down before it is built.

Report

The project includes a full written report in LaTeX, with architecture and ontology diagrams drawn in TikZ and its own bibliography.

See also

Pictures

Software agent ระบบแนะนำบริการสาธารณสุขไทย page 0001
Software agent ระบบแนะนำบริการสาธารณสุขไทย page 0001
Software agent ระบบแนะนำบริการสาธารณสุขไทย page 0009
Software agent ระบบแนะนำบริการสาธารณสุขไทย page 0009
Software agent ระบบแนะนำบริการสาธารณสุขไทย page 0012
Software agent ระบบแนะนำบริการสาธารณสุขไทย page 0012

In the knowledge graph

Health-rec paper and the 18 things it is linked to. Open any dot for its page, or explore it in the full graph.

TikZ · skill · Built withSWRL rules · skill · Built withOwlready2 · skill · Built withCompetency questions · technique · ImplementsAcademic output · person, role or place · ContextJSON-LD · dataset or file format · File formatsKnowledge graph construction from unstructured data · work · mentionsKG-from-text paperOntology & SPARQL cheat sheets · work · links hereOntology & SPARQL…Python · skill · Built withPythonKnowledge graphs · skill · Built withKnowledge graphsSPARQL · skill · Built withSPARQLOWL · skill · Built withOWLLaTeX · skill · Built withLaTeXOntology engineering · skill · Built withOntology eng.RDFS and OWL reasoning · technique · ImplementsRDFS and OWL reas…Ontology and knowledge graphs · theme · ThemesOntology and know…Chiang Mai University, Department of Computer Science · person, role or place · mentionsChiang Mai Univer…RDF Turtle · dataset or file format · File formatsRDF Turtle Health-recpaper
18 linked in the graph: 2 works9 skills2 techniques1 theme2 people, roles and places2 datasets and file formats
When
2023202420252026
Mar 2026 · 18 commits

As data

Everything on this page is read from triples in the knowledge graph. These are the 32 it states about Health-rec paper, and 6 more its ontology implies; the console runs SPARQL over the whole graph, here in your browser.

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@prefix id: <https://ask.chaipat.cc/id/> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix cj: <https://ask.chaipat.cc/ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

id:HealthRec
    a cj:Paper ;
    rdfs:label "Ontology-augmented health recommendation" ;
    cj:shortLabel "Health-rec paper" ;
    cj:context id:AcademicOutput ;
    cj:uses id:OWL ,
            id:KnowledgeGraphs ,
            id:SPARQL ,
            id:SWRL ,
            id:Owlready2 ,
            id:Python ,
            id:LaTeX ,
            id:OntologyEngineering ,
            id:TikZ ;
    cj:description "Ontology-augmented personal healthcare service recommendation for Thailand: an OWL 2 DL ontology of diseases, symptoms, services, facilities, locations and insurance, queried with SPARQL and reasoned over with SWRL rules. Built for an ontology-engineering course, with a full written report." ;
    cj:featured true ;
    cj:repository "https://github.com/AppleBoiy/onto-augmented-PHSRS"^^xsd:anyURI ;
    cj:firstCommit "2026-03-06"^^xsd:date ;
    cj:lastCommit "2026-03-15"^^xsd:date ;
    cj:commitCount 18 ;
    cj:kind "course project" ;
    cj:theme id:ThemeOntology ;
    cj:tier 1 ;
    cj:keyRank 8 ;
    cj:implements id:CompetencyQuestions , id:OntologyReasoning ;
    cj:handles id:TurtleFormat , id:JSONLD ;
    cj:mentions id:CMU , id:KGFromText ;
    cj:article "https://ask.chaipat.cc/wiki/HealthRec"^^xsd:anyURI ;
    cj:activeIn id:Y2026 ;
    cj:activity _:b74 .
Works 2
KG-from-text paperOntology & SPARQL cheat sheets