I spent the day in San Francisco yesterday attending part 2 of Neo4j's GenAI Graph Gathering. Along with the original session back in May, this was one of the…

Updating an ontology or graph data model has a lot of implications. How much change does the update cause, is the change breaking any up or downstream applic...

As I did my PhD in network and data science, graphs are really close to me. Now, if you would like to get a tast of why these fields are so exciting and…

Causal discovery with panel data? Welcome tigramite Python package. (Note that I am still unsure about causal discovery applications.) I can't find who…

In Parallel’s Intelligent Operating Model uses AI to align strategy with execution, improving value creation and operational efficiency to meet today’s challenges.

AIs will only be safe for general use when they have and use goals and values that are identical to those of humans. In theory, the particular goals and values – very much like Asimov's original Laws of Robotics – could be legislated and enforced, so that we would all be safe from harm from AI.

It’s decision time for Galaxy users as Samsung responds to new iPhone threat.

The world of graph technology is vast and evolving. We're here to help you make sense of the key trends and players.

A deep dive into LangChain’s implementation of graph construction with LLMs

PostgreSQL to Graph querying in less than 5 minutes

While PuppyGraph is less than a year old, it is already witnessing success with several enterprises, including Coinbase, Clarivate, Dawn Capital and Prevelant AI.

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.

A question-based extraction approach

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

We are launching a series of benchmarks to prove the value of GraphRAG for question & answering on complex documents. The process is simple, we ingest the…

🌐 Unlock the Power of Causal Graphs: Go Beyond Explainability with PyWhy! Hey, #GraphGeeks! Don’t miss Amy Hodler and Michelle Yi’s session at Open Data…

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...

Following its successful Series A funding, DevRev drives forward its mission to revolutionize customer support and product development.

Can Ontologies be seen as General Ledger for AI? Could that be a good way to audit AI systems delivering critical business outcomes? In my quest to develop a… | 69 comments on LinkedIn

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 about the different kinds of data involved in developing, using and monitoring foundation AI models and systems.

Learn how to overcome them using knowledge graphs and structured tools

Publishers of Foundations and Trends, making research accessible

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…