Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the...

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.

🕸️Building a LangGraph agent with graph memory The following community examples demonstrates building an agent using LangGraph. Graphiti is used to… | 24 comments on LinkedIn

🌟 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

Learn how to overcome them using knowledge graphs and structured tools

Don’t complicate things with graph DBs, QLs, or graph analytics.

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

Data semantics is harder than rocket science! Hear Juan Sequeda argue why enterprises need to be investing in semantics. https://lnkd.in/gKV-Prk4 High…

Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 13 comments on LinkedIn

“Chat with a PDF” is so 2023. In 2024 we turn 1,000 PDFs into knowledge. In 2023, GenAI exploded and everyone had a side project to "chat with a PDF." That… | 22 comments on LinkedIn

Graph RAG answers the big questions where text embeddings won’t help you.

🚀 Exciting New Research: "Graph-constrained Reasoning (GCR)" - Enabling Faithful KG-grounded LLM Reasoning with Zero Hallucination! 🧠 🎉 Proud to share our… | 11 comments on LinkedIn

GraphRAG explained with an imaginary quiz game and not a single line of source code

Applications of Graph approaches in RAG — current state

A GCP native, fully serverless implementation that you will replicate in minutes

In this video, I introduce LightRAG, a new, cost-effective retrieval augmented generation (RAG) method that combines knowledge graphs and embedding-based ret...

⛔ The current challenge in building KGs from unstructured documents using LLMs is ensuring that the extracted triplets fully capture the provided context. 🟢…

Data Structures for Hyper-Personalized Lesson Plans

Document Metadata Generation, File Directory & Two-Step Information Retrieval Processes

Learn how to turn CSV files into graph models using LLMs, simplifying data relationships, enhancing insights, and optimizing workflows.

“Ever wondered how to find or build Knowledge Graphs that power everything from recommendation engines to drug discovery?”

The 3 layers of Agentic Graph RAG 💬 The 3 layers of agentic graph RAG represent a significant advancement in AI-driven knowledge systems. These layers… | 17 comments on LinkedIn

Researchers created a knowledge graph based on 58 million journal papers to fuel personalized research ideas for scientists. Over 4400 ideas generated by the… | 29 comments on LinkedIn

Hello wonderful people, I’ve noticed some common misconceptions about graphs and AI circulating on LinkedIn, and I thought it might be helpful to share some insights to clarify these topics. I hope…

Glean | Glean’s knowledge graph and signals enables it to provide state-of-the-art hybrid search and generative AI that only gets better over time.

🧠 Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level. 🔎 Connecting an enterprise…

A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe

A recent post I wrote on LinkedIn hit a nerve. Talking about information/semantic rich projects failing due to the lack of organizational commitment is not new, for me or others in the field.