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Found 8 papers, 5 papers with code
Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
We refer to this subgraph as a task-oriented subgraph (TOSG), which contains a subset of task-related node and edge types in G. Training the task using TOSG instead of G alleviates the excessive computation required for a large KG.
Towards a GML-Enabled Knowledge Graph Platform
While training a GML model on KG, KGNet collects metadata of trained models in the form of an RDF graph called KGMeta, which is interlinked with the relevant subgraphs in KG.
KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science
Hence, this paper presents a scalable platform, KGLiDS, that employs machine learning and knowledge graph technologies to abstract and capture the semantics of data science artifacts and their connections.
A Universal Question-Answering Platform for Knowledge Graphs
Knowledge from diverse application domains is organized as knowledge graphs (KGs) that are stored in RDF engines accessible in the web via SPARQL endpoints.
ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots
Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for extracting information easily and effectively.
Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning
We demonstrate the efficiency and usefulness of Serenity's analysis in two applications: code completion and automated machine learning.
Federated Data Science to Break Down Silos [Vision]
Similar to Open Data initiatives, data science as a community has launched initiatives for sharing not only data but entire pipelines, derivatives, artifacts, etc.
A Scalable AutoML Approach Based on Graph Neural Networks
AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner.
