Knowledge Tree
Relations (1)
related 2.58 — strongly supporting 5 facts
Named Entity Recognition (NER) is a foundational component of knowledge graph construction, as evidenced by its use in neural network-based construction techniques [1] and its role in joint extraction processes to improve performance [2], [3]. Furthermore, research specifically applies deep learning-based NER to build knowledge graphs for domains like geological hazards [4], and academic literature explicitly links the two as core perspectives in the state-of-the-art construction from text [5].
Facts (5)
Sources
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org 2 facts
claimJointly performing Named Entity Recognition and Relationship Extraction reduces error propagation and improves overall performance in Knowledge Graph construction.
claimJointly performing Named Entity Recognition and Relationship Extraction reduces error propagation and improves overall performance in Knowledge Graph construction.
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 1 fact
referenceFan et al. (2020) utilized deep learning-based named entity recognition for knowledge graph construction specifically applied to geological hazards.
The construction and refined extraction techniques of knowledge ... nature.com 1 fact
claimNeural network-based techniques for knowledge graph construction, such as SpaCy, NLTK, and ltp, utilize a blend of rules and statistical models for Named Entity Recognition (NER) tasks.
The State of the Art on Knowledge Graph Construction from Text zenodo.org 1 fact
referenceThe presentation titled 'The State of the Art on Knowledge Graph Construction from Text: Named Entity Recognition and Relation Extraction Perspectives' covers benchmark dataset resources and neural models for knowledge graph construction tasks.