Knowledge Tree
entity fusion
Facts (11)
Sources
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 11 facts
claimProperty matching is essential for entity resolution and entity fusion, as it helps determine matching entities based on property similarity and allows for the combination of equivalent properties to avoid redundancy.
claimEntity fusion is the process of combining matching entities to enrich information about an entity in a uniform way, following the entity resolution step.
claimKnowledge graph-specific approaches have limitations regarding scalability to many sources, support for incremental updates, metadata management, ontology management, entity resolution and fusion, and quality assurance.
claimThe SAGA knowledge graph construction solution utilizes several truth discovery and source reliability-based fusion methods for entity fusion.
claimData integration and canonicalization in knowledge graphs involve entity linking, entity resolution, entity fusion, and the matching and merging of ontology concepts and properties.
claimThe unnamed artist-focused knowledge graph construction process applies the union of matching entities but leaves entity fusion (value consolidation) to consuming applications.
claimThe DRKG project utilizes a simple form of entity fusion to normalize entity identifiers.
claimOpen knowledge graph-specific approaches currently face limitations in scalability to many sources, support for incremental updates, and several technical areas including metadata management, ontology management, entity resolution/fusion, and quality assurance.
procedureEntity fusion in the SAGA system involves harmonizing conflicting entity attribute values based on truth discovery methods and source reliability to create consistent entities.
claimEntity fusion is the least supported task among the knowledge graph construction solutions considered in the study, with none of the dataset-specific knowledge graphs performing classical, sophisticated entity fusion.
claimQuality assurance is necessary throughout the entire Knowledge Graph construction process, including source selection, data cleaning, knowledge extraction, ontology evolution, and entity fusion.