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…

Our manuscript on using Large Language Models and Retrieval Augmented Generation for creating ontology terms is up on arXiv! https://lnkd.in/d62JPtiH, lead…

Over the past few months, I've been immersed in an exciting experiment, leveraging OpenAI's advanced language models to transform unstructured text into RDF (Resource Description Framework) triples. The journey, as thrilling as it has been, is filled with ongoing challenges and learning experiences.

Learn how to build knowledge graphs using Python and large language models (LLMs) to create intricate interconnected knowledge maps

Large language models (LLMs) fit parameters (features in data topography) to a particular dataset, such as text scraped off the web and conformed to a training set.  Logical data models (LDMs), by contrast, model what becomes shared within entire systems. They bring together the data in a system with the help of various kinds of… Read More »How the LDMs in knowledge graphs can complement LLMs

This post talks about the nature and key characteristics of knowledge graphs. It also outlines the benefits of formal semantics and how…

Go beyond typical RAG strategies

Augmenting Large Language Models with Hybrid Knowledge Architectures 🧐 Vector search or Knowledge graph ? Why not both at the same time… Retrieval…

Here's the video for my talk @ K1st World Symposium 2023 about the intersections of KGs and LLMs: https://lnkd.in/gugB8Yjj and also the slides, plus related…

"Okay guys, if you want to read one article about ontologies, taxonomy, and data, this is the one. Congratulations Graham.

While LLMs have shown remarkable prowess in understanding and generating text, they have a critical limitation.

Despite the fact that it affects our lives on a daily basis, most of us are unfamiliar with the concept of a knowledge graph. When we ask Alexa about tomorrow's weather or use Google to look up the latest news on climate change, knowledge graphs serve as the foundation of today's cutting-edge information systems. In addition, knowledge graphs have the potential to elucidate, assess, and substantiate information produced by Deep Learning models, such as Chat-GPT and other large language models. Knowledge graphs have a wide range of applications, including improving search results, answering questions, providing recommendations, and developing explainable AI systems. In essence, the purpose of this course is to provide a comprehensive overview of knowledge graphs, their underlying technologies, and their significance in today's digital world.

The evidence for the massive impact of KGs in NLQ keeps piling up - Here's one more paper that shows that knowledge graph based RAG (retrieval-augmentation)…

We’re excited to announce a new AI-powered Skills in Viva service that will help organizations understand workforce skills and gaps, and deliver personalized skills-based experiences.

One of our main focuses at Zazuko GmbH is to support government organizations in publishing multidimensional data in RDF format. To this end, we utilize the cube.

This is the first of a series of blogs wherein I describe what we believe is a superior pattern for creating highly performant, real-world…

On August 14, 2023, the paper Natural Language is All a Graph Needs by Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu and Yongfeng Zhang…

Knowledge graphs (KGs) comprise entities interconnected by relations of different semantic meanings. KGs are being used in a wide range of applications. However, they inherently suffer from incompleteness, i.e. entities or facts about entities are missing. Consequently, a larger body of works focuses on the completion of missing information in KGs, which is commonly referred to as link prediction (LP). This task has traditionally and extensively been studied in the transductive setting, where all entities and relations in the testing set are observed during training. Recently, several works have tackled the LP task under more challenging settings, where entities and relations in the test set may be unobserved during training, or appear in only a few facts. These works are known as inductive, few-shot, and zero-shot link prediction. In this work, we conduct a systematic review of existing works in this area. A thorough analysis leads us to point out the undesirable existence of diverging terminologies and task definitions for the aforementioned settings, which further limits the possibility of comparison between recent works. We consequently aim at dissecting each setting thoroughly, attempting to reveal its intrinsic characteristics. A unifying nomenclature is ultimately proposed to refer to each of them in a simple and consistent manner.

This week Alexander Erdl and I did our last #GoingMeta of the year, and we talked about Advanced #RAG patterns with #KnowledgeGraphs and vector search. We…