الفهرس | Only 14 pages are availabe for public view |
Abstract Taking all together the experimental results, evaluation, and discussions; the proposed framework succeeded in fully integrating heterogeneous data sources and | P a g e 99 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. |