This describes the requirements identified for the ontology

Leveraging Graphs to Advance Chain-of-Thought Reasoning ⛓ Chain-of-thought (CoT) prompting has rapidly emerged as a technique to substantially improve the…

Knowledge Graphs are, IMHO, the best way to structure data for any subsequent analysis. The problem is that the majority of data that is…

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.

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.

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…

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…

Knowledge graph embedding models (KGEMs) developed for link prediction learn vector representations for graph entities, known as embeddings. A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, i.e., position similar entities close to one another. This desirable property make KGEMs widely used in downstream tasks such as recommender systems or drug repurposing. Yet, the alignment of graph similarity with embedding space similarity has rarely been formally evaluated. Typically, KGEMs are assessed based on their sole link prediction capabilities, using ranked-based metrics such as Hits@K or Mean Rank. This paper challenges the prevailing assumption that entity similarity in the graph is inherently mirrored in the embedding space. Therefore, we conduct extensive experiments to measure the capability of KGEMs to cluster similar entities together, and investigate the nature of the underlying factors. Moreover, we study if different KGEMs expose a different notion of similarity. Datasets, pre-trained embeddings and code are available at: https://github.com/nicolas-hbt/similar-embeddings.

Knowledge Graph Embeddings as a Bridge between Symbolic and Subsymbolic AI 🌉 The Resurgence of Structure The pendulum in AI is swinging back from purely…