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Showing 1 to 8 of 8 for “"Knowledge Graph Construction"”.
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Methods for Knowledge Graph Construction from Text Collections: Development and Applications
… pressing challenges for extracting actionable knowledge for several application scenarios. However, the extraction of rich semantic knowledge demands the deployment of scalable and flexible automatic methods adaptable across text genres and schema specifications. Moreover, the full potential of …
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Towards Knowledge Graph Construction From Unstructured Text with LLMs, Triple Identification and Alignment to Wikidata
… digital text has underscored the pivotal role of Knowledge Graphs (KGs) in structuring, managing, and deriving value from unstructured data. However, a vast portion of textual content remains unstructured, posing critical challenges for the automatic construction and enrichment of KGs, …
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Towards Knowledge Graph Construction From Unstructured Text with LLMs, Triple Identification and Alignment to Wikidata
… digital text has underscored the pivotal role of Knowledge Graphs (KGs) in structuring, managing, and deriving value from unstructured data. However, a vast portion of textual content remains unstructured, posing critical challenges for the automatic construction and enrichment of KGs, …
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Semantic pattern discovery in open information extraction
… segmentation method which splits the dependency graph of sentences at noun or verb level and enables pattern extraction between distantly placed entities; (2) it extracts meta patterns and handles its pattern sparsity problem by introducing a novel idea of iterative frequent pattern mining and …
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Scalable information extraction with large language models
… transforms unstructured text into structured knowledge such as entities and relations, and is fundamental to applications including knowledge graph construction, information retrieval, question answering, and domain-specific document understanding. Although large language models (LLMs) have …
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Design and Evaluation of Network Algorithms and Deep Learning Models in Systems Biology and Biomedicine
… present ICoN, an unsupervised coattention-based graph neural network model for integrating heterogeneous protein-protein interaction networks. ICoN learns joint embeddings across multiple networks and captures complementary biological evidence. ICoN surpassed individual networks across three …
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Consistency-aware and LLM-assisted methods for named entity recognition
… extraction, biomedical text mining, and knowledge graph construction. Despite significant progress with neural models, most existing NER approaches process sentences independently, which can lead to inconsistent predictions for repeated entity mentions across a document. Moreover, the …
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Scientific knowledge extraction from massive text data
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms