entity

HKGB

Also known as: Health Knowledge Graph Builder

Facts (13)

Sources

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 13 facts

claimWorldKG and HKGB utilize semi-automatic methods to build initial ontologies, which is more advanced than manual ontology construction.

referenceHKGB is an inclusive, extensible, intelligent, semi-auto-constructed knowledge graph framework for healthcare that incorporates clinicians' expertise, as described by Y. Zhang et al. in Information Processing & Management in 2020.

claimThe HKGB knowledge graph construction solution provides a description of its entity resolution (ER) process that is too vague to allow for a definitive assessment of its capabilities.

measurementThe HKGB platform accepts annotations for its knowledge graph only if there is high agreement across annotators, defined as exceeding a confidence threshold of 0.81.

claimIn addition to construction tools, the HKGB platform provides three graph tools for data discovery, extraction, and link prediction to support domain-related applications.

procedureThe HKGB platform divides disease-specific information ingestion into two phases: building the Concept Knowledge Graph (ontology) and building the Instance Knowledge Graph.

claimThe DRKG, HKGB, and SAGA knowledge graph construction solutions use machine learning-based link prediction on graph embeddings to find further knowledge for knowledge completion.

claimEntity linking in knowledge graphs is performed using various methods, including dictionary-based approaches relying on gathered synonyms in AI-KG, human interaction in XI, or entity resolution in HKGB.

measurementThe HuadingKG, developed using the HKGB, contains approximately 85 million entities and 265 million relations, covering information about cardiovascular diseases and the Knee Osteoarthritis domain.

claimThe Health Knowledge Graph Builder (HKGB) is a platform designed to semi-automatically construct clinical knowledge graphs with heavy human-in-the-loop involvement, consuming Electronic Medical Records (EMR) as input and producing graph data in OWL and RDF formats.

claimThe HKGB knowledge graph construction solution relies heavily on user interaction for quality assurance.

claimNELL and HKGB are knowledge graph systems that can identify new entity and relation types in input data for addition to the ontology after manual confirmation.

procedureThe human interaction process in the HKGB platform involves four steps: (1) inspecting new concepts or relations to approve recommendations, (2) adding medical synonym entities based on instances, (3) annotating unstructured data based on instances and relations of the current knowledge graph, and (4) defining mapping rules from Electronic Medical Records to RDF and extracting concepts, entities, and relations.