Knowledge Tree
entity
Facts (10)
Sources
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 9 facts
referenceAn incremental approach based on correlation clustering, proposed in reference [213], maintains previous clusters within a similarity graph and uses the updated graph to determine clusters for new entities while also repairing previous clusters through splitting, merging, or moving entities.
claimCorrectness in a knowledge graph implies the validity of information (accuracy) and consistency, meaning each entity, concept, relation, and property is canonicalized with a unique identifier and included exactly once.
claimConstructing knowledge graphs requires selecting a preferred name for matching attributes when records disagree, ensuring consistency across entities of the same type to facilitate effective querying.
claimData fusion is a main step in data integration because it combines information from several entities into one enriched entity and entails resolving inconsistencies in the data.
referenceThe approach described in reference [66] supports a light-weight cluster repair method called 'n-depth reclustering,' which only considers entities close to new entities in the updated similarity graph for changing clusters.
referenceIncremental approaches described in references [65, 66] support optimized clustering decisions for duplicate-free (clean) data sources where at most one entity can participate per cluster.
claimThe smallest unit of information is defined as a statement or fact; for RDF this describes a triple, while for Property Graph Models (PGM) this can be assigning a property-value, adding a type label to an entity, or adding a relation between two nodes.
procedureThe 'max-both' clustering strategy adds an entity from a set of new entities to the most similar cluster only when no other new entity is more similar to that cluster than the entity being considered.
claimData fusion is the process of merging multiple records of the same real-world entity into a single, consistent, and clean representation, as defined in reference [224].
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 1 fact
formulaThe forward propagation of R-GCN is calculated as: h_k^(l+1) = sigma(sum_{r in R} sum_{j in N_k^r} (1/c_{k,r}) W_r^(l) h_j^(l) + W_0^(l) h_k^(l)), where h_k^(l+1) is the hidden state of entity k in the l-th layer, N_k^r denotes a neighbor collection of entity k and relation r, c_{k,r} is the normalization process, and W_r and W_0 are weight matrices.