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Unlocking LLM Power with Organizational KG Ontologies | VectorHub by Superlinked
Unlocking LLM Power with Organizational KG Ontologies | VectorHub by Superlinked
Large Language Models (LLMs) are revolutionizing AI capabilities, but organizations face challenges in reducing inaccuracies and protecting valuable data. Knowledge Graphs offer a solution, helping improve LLM accuracy and safeguard organizational data assets. Learn how implementing Knowledge Graphs can address these critical issues and maintain competitiveness in the AI landscape.
·superlinked.com·
Unlocking LLM Power with Organizational KG Ontologies | VectorHub by Superlinked
Data Object Graph (DOG), queryable, adaptable traversable hybrid data and execution graphs built for AI and traditional analytics use-cases in complex business processes
Data Object Graph (DOG), queryable, adaptable traversable hybrid data and execution graphs built for AI and traditional analytics use-cases in complex business processes
From DAGs to DOGs: Data Object Graph (DOG), queryable, adaptable traversable hybrid data and execution graphs built for AI and traditional analytics use-cases… | 59 comments on LinkedIn
Data Object Graph (DOG), queryable, adaptable traversable hybrid data and execution graphs built for AI and traditional analytics use-cases in complex business processes
·linkedin.com·
Data Object Graph (DOG), queryable, adaptable traversable hybrid data and execution graphs built for AI and traditional analytics use-cases in complex business processes
Connecting the Facts: SAP HANA Cloud’s Knowledge Graph Engine for Business Context
Connecting the Facts: SAP HANA Cloud’s Knowledge Graph Engine for Business Context
Introduction: In Q1 2025, SAP is planning to release the knowledge graph engine as a new capability for SAP HANA Cloud platform. This new multi-model engine allows businesses to connect and analyze information more effectively. SAP HANA Cloud will enable smarter applications and improve user experie...
·community.sap.com·
Connecting the Facts: SAP HANA Cloud’s Knowledge Graph Engine for Business Context
WorldFAIR (D2.3) Cross-Domain Interoperability Framework (CDIF) (Report Synthesising Recommendations for Disciplines and Cross-Disciplinary Research Areas)
WorldFAIR (D2.3) Cross-Domain Interoperability Framework (CDIF) (Report Synthesising Recommendations for Disciplines and Cross-Disciplinary Research Areas)
The Cross-Domain Interoperability Framework (CDIF) is designed to support FAIR implementation by establishing a ‘lingua franca’, based on existing standards and technologies to support interoperability, in both human- and machine-actionable fashion. CDIF is a set of implementation recommendations, based on profiles of common, domain-neutral metadata standards which are aligned to work together to support core functions required by FAIR. This report presents a core set of five CDIF profiles, which address the most important functions for cross-domain FAIR implementation.  Discovery (discovery of data and metadata resources) Data access (specifically, machine-actionable descriptions of access conditions and permitted use) Controlled vocabularies (good practices for the publication of controlled vocabularies and semantic artefacts) Data integration (description of the structural and semantic aspects of data to make it integration-ready) Universals (the description of ‘universal’ elements, time, geography, and units of measurement). Each of these profiles is supported by specific recommendations, including the set of metadata fields in specific standards to use, and the method of implementation to be employed for machine-level interoperability. A further set of topics is examined, establishing the priorities for further work. These include: Provenance (the description of provenance and processing) Context (the description of ‘context’ in the form of dependencies between fields within the data and a description of the research setting) Perspectives on AI (discussing the impacts of AI and the role that metadata can play) Packaging (the creation of archival and dissemination packages)  Additional Data Formats (support for some of the data formats not fully supported in the initial release, such as NetCDF, Parquet, and HDF5).  In each of these topics, current discussions are documented, and considerations for further work are provided. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.
·zenodo.org·
WorldFAIR (D2.3) Cross-Domain Interoperability Framework (CDIF) (Report Synthesising Recommendations for Disciplines and Cross-Disciplinary Research Areas)
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
🌟 TGB 2.0 @NeurIPS 2024 🌟 We are very happy to share that our paper TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs… | 11 comments on LinkedIn
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
·linkedin.com·
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
A data for AI taxonomy
A data for AI taxonomy
Learn about the different kinds of data involved in developing, using and monitoring foundation AI models and systems.
·theodi.org·
A data for AI taxonomy
What Do D&A Leaders Need to Know About Data Products?
What Do D&A Leaders Need to Know About Data Products?
Attention #Data and #Aalytics Leaders. Our team has fielded over 500 inquires on the subject of #DataProducts. In fact it is now one of the most popular topics… | 28 comments on LinkedIn
What Do D&A Leaders Need to Know About Data Products?
·linkedin.com·
What Do D&A Leaders Need to Know About Data Products?
Knowledge Graph In-Context Learning
Knowledge Graph In-Context Learning
Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 13 comments on LinkedIn
Knowledge Graph In-Context Learning
·linkedin.com·
Knowledge Graph In-Context Learning
Graph-constrained Reasoning
Graph-constrained Reasoning
🚀 Exciting New Research: "Graph-constrained Reasoning (GCR)" - Enabling Faithful KG-grounded LLM Reasoning with Zero Hallucination! 🧠 🎉 Proud to share our… | 11 comments on LinkedIn
Graph-constrained Reasoning
·linkedin.com·
Graph-constrained Reasoning
The current challenge in building KGs from unstructured documents using LLMs is ensuring that the extracted triplets fully capture the provided context
The current challenge in building KGs from unstructured documents using LLMs is ensuring that the extracted triplets fully capture the provided context
⛔ The current challenge in building KGs from unstructured documents using LLMs is ensuring that the extracted triplets fully capture the provided context. 🟢…
·linkedin.com·
The current challenge in building KGs from unstructured documents using LLMs is ensuring that the extracted triplets fully capture the provided context