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The impacts of complexity on the growth and efficiency of big enterprises & how knowledge graphs enable richer insights from data in less time

In my previous post,

Reasoning with Knowledge Graph Clustering in Retrieval-Augmented Generation Systems 🔲 ⚫ Retrieval-augmented generation (RAG) systems have gained immense… | 35 comments on LinkedIn

The secret of AI: it’s all about building your Knowledge Graphs. This open secret deserves more spotlight: a LLM, at its core, is fundamentally a database, a… | 69 comments on LinkedIn

Dive deep into a case study showcasing best practices and pitfalls to avoid, ensuring your journey to a Knowledge Panel is successful.

Find out if using Schema markup increases SEO rankings or correlates with user signals, featuring WordLift experts and adjunct professor Steve Wiideman.

Continuing our exploration of Knowledge Graphs and Ontologies, this next part delves into hands-on implementation. Let’s transform flat CSV…

We’ll delve into the world of ontologies and understand their significance. Additionally, witness the magic of Python as it transforms flat

Injecting KGs in RAG in pre-processing, post-processing, chunk extraction, document and contextual hierarchies, with a concrete example.

Recently there has been much excitement related to Artificial Intelligence and Knowledge Graphs, especially regarding the emerging…

The previous articles of this series, such as Getting Started, have introduced SHACL as a language for representing structural constraints on (knowledge) graphs: classes, attributes, relationships and shapes. These language features describe the formal characteristics that valid instances need to ha

Taxonomies – coherent collections of facts with taxonomic relations – play a crucial and growing role in how we – and AIs – structure and index knowledge. Taken in the context of an "anatomy" of knowledge, taxonomic relations – like instanceOf and subcategoryOf – form the skeleton, a sketchy, incomp

YouTube channel of the COST Action "Distributed Knowledge Graphs" (DKG). We investigate Knowledge Graphs that are published in a decentralised fashion, thus forming a distributed system. COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. The Action is funded by the Horizon 2020 Framework Programme of the European Union.

Yesterday, I re-shared a huge list of Python visualisation tools - and now, here comes a list of network visualisation tools (these two lists certainly… | 74 comments on LinkedIn

Python open source projects; natural language meets graph technologies; graph topological transformations; graph levels of detail (abstraction layers)

Use Gartner’s impact radar for generative AI to plan investments and strategy with four key themes in mind: ☑️Model-related innovations ☑️Model performance and AI safety ☑️Model build and data-related ☑️AI-enabled applications Explore all 25 technologies and trends: https://www.gartner.com/en/articles/understand-and-exploit-gen-ai-with-gartner-s-new-impact-radar

What do we mean when we say something is a kind of thing? I’ve been wrestling with that question a great deal of late, partly because I think the role of the ontologist transcends the application of knowledge graphs, especially as I’ve watched LLMs and Llamas become a bigger part of the discussion.

This blog post summarizes the findings of our new contribution:

Neural algorithmic reasoning focuses on building models that can execute classic algorithms. It allows one to combine the advantages of neural networks, such as handling raw and noisy input data, with theoretical guarantees and strong generalization of algorithms. Assuming we have a neural network capable of solving a classic algorithmic task, we can incorporate it into a more complex pipeline and train end-to-end. For instance, if we have a neural solver aligned to the shortest path problem, it can be used as a building block for a routing system that accounts for complex and dynamically changing traffic conditions. In our work [ref1], we study algorithmic reasoners trained only from input-output pairs, in contrast to current state-of-the-art approaches that utilize the trajectory of a given algorithm. We propose several architectural modifications and demonstrate how standard contrastive learning techniques can regularize intermediate computations of the models without appealing to any predefined algorithm’s trajectory.

Fusion Knowledge Graphs and Language Models Through Compatible Generative Modeling 🌙 Knowledge graphs (KGs) and large language models (LLMs) have…

Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.

“Everything is everything/What is meant to be, will be.” – Lauryn Hill Polyhierarchy Polyhierarchy is “a controlled vocabulary structure in which some terms belong to more than one hierarchy.…

Orchestrating Efficient Reasoning Over Knowledge Graphs with LLM Compiler Frameworks 🔵 (published in Towards Data Science) Recent innovations in Large…

Nature Machine Intelligence - Deep learning is a powerful method to process large datasets, and shown to be useful in many scientific fields, but models are highly parameterized and there are often...

Optimizing Retrieval-Augmented Generation (RAG) by Selective Knowledge Graph Conditioning 🔝 (published in Towards Data Science) Generative pre-trained… | 15 comments on LinkedIn

A broad and deep body of on-going research – hundreds of experiments! – has shown quite conclusively that knowledge graphs are essential to guide, complement, and enrich LLMs in systematic ways. The very wide variety of tests over domains and possible combinations of KGs and LLMs attests to the robu

Building robust apps and business systems on graph databases has proven really hard, but it does not need to be. This is why.

Hallucination is a common problem when working with large language models (LLMs). LLMs generate fluent and coherent text but often generate…