Knowledge Graph ↔ entity resolution

Relations (1)

related 12.00 — strongly supporting 12 facts

Entity resolution is a critical technical process for the construction, maintenance, and quality assurance of knowledge graphs, as evidenced by its role in data integration [1], fusion [2], and the handling of streaming data {fact:9, fact:10}.

Facts (12)

Sources

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 9 facts

claimStreaming-like data ingestion into a knowledge graph requires support for dynamic, real-time matching of new entities with existing knowledge graph entities.

claimBlocking for incremental or streaming Entity Resolution requires identifying a subset of existing Knowledge Graph entities for matching to ensure efficiency, as Knowledge Graphs are typically large and growing.

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.

claimRecent approaches to entity resolution for knowledge graphs utilize multi-source big data techniques, Deep Learning, or knowledge graph embeddings.

claimMost existing entity resolution approaches for knowledge graphs are designed for static or batch-like processing where matches are determined within or between datasets of a fixed size.

claimData integration and canonicalization in knowledge graphs involve entity linking, entity resolution, entity fusion, and the matching and merging of ontology concepts and properties.

claimEntity Resolution and Fusion is the process of identifying matching entities and merging them within a knowledge graph.

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.

claimNeural methods for entity resolution in knowledge graphs have recently faced increased scrutiny following a period of significant hype.

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv 2 facts

claimFuture research could improve the quality and reliability of the knowledge graphs used by CoE by integrating advanced methods such as entity resolution (Binette et al., 2022) and entity linking (Shen et al., 2021).

Unlock the Power of Knowledge Graphs and LLMs - TopQuadrant topquadrant.com Steve Hedden · TopQuadrant 1 fact

claimLarge language models enable faster knowledge graph creation and curation by performing entity resolution, automated tagging of unstructured data, and entity and class extraction.