Knowledge graphs have revolutionized the way we organize and analyze data. By visually depicting entities and their interconnections, they…

Welcome to the world of Semantic Web! 🌐 It’s a broad term where ideas and technologies come together to tackle the overwhelming amount of…

Navigating the EU AI Act: Introducing the AI Act Requirements Graph As the EU AI Act moves closer to becoming law, understanding its complex web of… | 11 comments on LinkedIn

In this blog post, we’ll be focusing on how to write the retrieval query that supplements or grounds the LLM’s answer.

Overview

Grab some Oreos and let's chow down some #ontology basics. As an English Literature major, we did talk about quite a few philosophical concepts, but the… | 25 comments on LinkedIn

Recently I watched Andrew Ng and Andreas Kollegger course “Knowledge Graphs for RAG” available at deeplearning.ai. The course builds…

InBox: Enhancing Recommender Systems with Knowledge Graph-based Interest Box Embeddings ... 🧠 (Alibaba Group / Ant Group) Imagine if AI could understand not…

I keep checking in on the Solid project. But I’m baffled by its lack of functionality. I’ve written up some of my questions.

The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques. This paper introduces a novel methodology, the Knowledge Graph Large Language Model Framework (KG-LLM), which leverages pivotal NLP paradigms, including chain-of-thought (CoT) prompting and in-context learning (ICL), to enhance multi-hop link prediction in KGs. By converting the KG to a CoT prompt, our framework is designed to discern and learn the latent representations of entities and their interrelations. To show the efficacy of the KG-LLM Framework, we fine-tune three leading Large Language Models (LLMs) within this framework, employing both non-ICL and ICL tasks for a comprehensive evaluation. Further, we explore the framework's potential to provide LLMs with zero-shot capabilities for handling previously unseen prompts. Our experimental findings discover that integrating ICL and CoT not only augments the performance of our approach but also significantly boosts the models' generalization capacity, thereby ensuring more precise predictions in unfamiliar scenarios.

Bob Kasenchak, Factor One of the regular tasks we undertake when starting an engagement with a new client involves cataloging and evaluating existing taxonomies in their business information ecosystem. But not all taxonomies are created equal; or, perhaps more specifically, not all taxonomies serve

Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).

A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain Editor's Note: the following is a guest blog post from Tomaz Bratanic, who focuses on Graph ML and GenAI research at Neo4j. Neo4j is a graph database and analytics company which helps

Editor’s note: Sumit Pal is a speaker for ODSC East this April 23-25. Be sure to check out his talk, “Building Knowledge Graphs,” there! Graphs and Knowledge Graphs (KGs) are all around us. We use them every day without realizing it. GPS leverages graph data structures and databases to plot...

There is an old saying that Democracy is the worst form of government known to mankind, except for all others. The same can be said for the Resource Description Framework or RDF.

Abstraction is an important concept in software development, but to fully embrace the powers of AI, we now need to introduce abstraction into our data… | 74 comments on LinkedIn

As LLMs turn turn data processing into semantic processing, we believe ETL processes will turn into ECL processes.

The Limitations of Cosine Similarity and the Case for Knowledge Graphs in AI ◼ As artificial intelligence (AI) systems become increasingly integrated into…

Today is a pretty special day. In some sense, this is the day I’ve been waiting for all my life. The day that we figure out how to make…

An overview of knowledge graphs and their main use cases

Building Knowledge Graphs in RAG is not just about using graphs as data stores, but also as a means for structured reasoning.

A word of caution from Netflix against blindly using cosine similarity as a measure of semantic similarity: https://lnkd.in/gX3tR4YK They study linear matrix… | 12 comments on LinkedIn

Introducing PyGraft: A Powerful Tool for Generating Synthetic Knowledge Graphs ... A new open-source tool called PyGraft is now available that enables the… | 13 comments on LinkedIn

Here are the slides I used for my 09-FEB-2024 keynote presentation "Knowledge Graphs: An Industry Perspective" at Dagstuhl Seminar 24061: "Are Knowledge Graphs…

I am thrilled to introduce a new AI Study Guide (https://lnkd.in/g4rPZVHW) dedicated to Tony Seale, another of my favorite authors, thought leaders, and…

Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some tasks, a few KGs established themselves as standard benchmarks. However, recent works outline that relying on a limited collection of datasets is not sufficient to assess the generalization capability of an approach. In some data-sensitive fields such as education or medicine, access to public datasets is even more limited. To remedy the aforementioned issues, we release PyGraft, a Python-based tool that generates highly customized, domain-agnostic schemas and KGs. The synthesized schemas encompass various RDFS and OWL constructs, while the synthesized KGs emulate the characteristics and scale of real-world KGs. Logical consistency of the generated resources is ultimately ensured by running a description logic (DL) reasoner. By providing a way of generating both a schema and KG in a single pipeline, PyGraft's aim is to empower the generation of a more diverse array of KGs for benchmarking novel approaches in areas such as graph-based machine learning (ML), or more generally KG processing. In graph-based ML in particular, this should foster a more holistic evaluation of model performance and generalization capability, thereby going beyond the limited collection of available benchmarks. PyGraft is available at: https://github.com/nicolas-hbt/pygraft.

Using vector databases in retrieval-augmented generation (RAG) raises concerns about relevance, since the info retrieved may not be semantically related to the…

KGLM-Loop: A Bi-Directional Data Flywheel for Knowledge Graph Refinement and Hallucination Detection in Large Language Models ☀ 🌑 In the pursuit of…

We've been hearing the term "Semantic layer" without truly understanding the semantics of it. So, here is episode 11 of #DnABytes and today's topic is:…