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Found 7 papers, 7 papers with code
Explaining word embeddings with perfect fidelity: Case study in research impact prediction
Best performing approaches for scholarly document quality prediction are based on embedding models, which do not allow direct explanation of classifiers as distinct words no longer correspond to the input features for model training.
The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies
This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies.
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Translational research requires data at multiple scales of biological organization.
Gene Set Summarization using Large Language Models
This is typically done as a statistical enrichment analysis that measures the over- or under-representation of biological function terms associated with genes or their properties, based on curated assertions from a knowledge base (KB) such as the Gene Ontology (GO).
Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning
Creating knowledge bases and ontologies is a time consuming task that relies on a manual curation.
Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions
NP-KG is a heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature, constructed with the Phenotype Knowledge Translator framework and the semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler.
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs.
