الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Taking all together the experimental results, evaluation, and discussions; the
proposed framework succeeded in fully integrating heterogeneous data sources and
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successfully built a standard-based knowledge graph suitable as a knowledge base for
healthcare applications with query results having a precision of 0.88, recall of 0.53 and F1
score of 0.66 for the database used for evaluation. The knowledge graph nodes covered all
diseases and symptoms from the standard ontologies, and fully integrated two standardized
ontologies. The knowledge graph considered that synonyms are represented by the same
node, thus avoiding redundancy, and unnecessary growth of the graph size. The smaller
graph size has a positive impact on reducing the response time for any healthcare querying
system. Each of the linked graph nodes has a unique identifier and IRI properties that are
universal standards and independent of a specific language, thus the graph could easily be
adjusted to serve any language. The proposed framework generated a knowledge graph
that is fully integrated, dynamic, scalable, easily reproducible, reliable, and practically
efficient. The cancer use case has proven that the cancer subgraph could serve as a separate
graph for cancer-related healthcare systems. The knowledge graph is evaluated based on
13 dimensions compared to other related work. The graph representation has provided a
way for querying and reasoning the graph using one of the reasoning implementation
paths used for popular system checkers, thus it could be a base for an advisor expert system.