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

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

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.

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

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

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…

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

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)…

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…

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…

Large language models (LLMs), such as ChatGPT and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and newly found emergent capability (e.g., reasoning). While LLMs are mainly designed to process pure texts, there are many real-world scenarios where text data are associated with rich structure information in the form of graphs (e.g., academic networks, and e-commerce networks) or scenarios where graph data are paired with rich textual information (e.g., molecules with descriptions). Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalized to graph scenarios (i.e., graph-based reasoning). In this paper, we provide a systematic review of scenarios and techniques related to large language models on graphs. We first summarize potential scenarios of adopting LLMs on graphs into three categories, namely pure graphs, text-rich graphs, and text-paired graphs. We then discuss detailed techniques for utilizing LLMs on graphs, including LLM as Predictor, LLM as Encoder, and LLM as Aligner, and compare the advantages and disadvantages of different schools of models. Furthermore, we mention the real-world applications of such methods and summarize open-source codes and benchmark datasets. Finally, we conclude with potential future research directions in this fast-growing field. The related source can be found at https://github.com/PeterGriffinJin/Awesome-Language-Model-on-Graphs.

When reading lengthy or intricate texts, keeping an overview of different dependencies within the context is crucial. Traditionally, humans achieve this through note-taking or mentally creating a concept map. Now imagine having AI at hand which generates such a map for you. Even better, the…

Enhancing AI Accuracy: Telco Network Knowledge Graph's Role in Overcoming LLM Hallucinations LLMs are known to create responses that, while appearing valid…

I really enjoyed the latest #UnconfuseMe with Bill Gates and Yejin Choi.  Yejin's research is on symbolic knowledge distillation, which means they take large…

The advent of large language models (LLMs) , trained on vast amounts of text data, has been one of the most significant breakthroughs in…

Note: This article and the underlying LLM application were developed with Alexander Gilmore, Associate Consulting Engineer at Neo4j.

Before gearing up for yet another AI project, ask if it's really the best approach.

A method to convert any text corpus into a Knowledge Graph using Mistral 7B.

Investing in Knowledge Graph provides higher accuracy for LLM-powered question-answering systems. And ultimately, to succeed in this AI world, enterprises must… | 23 comments on LinkedIn

Introducing MechGPT 🦾🤖 This project by Markus J. Buehler is one of the coolest use cases of 1) fine-tuning an LLM, and 2) generating a knowledge graph that… | 33 comments on LinkedIn

So, what are basically Knowledge Graphs and why do we need to use them?

Learn how to get GPT to ask complex questions in any language

Natural language processing made easy

Where does current work on graph technologies intersect with large language models, for building AI apps?

Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge and experience hallucinations during reasoning, which can lead to incorrect reasoning processes and diminish their performance and trustworthiness. Knowledge graphs (KGs), which capture vast amounts of facts in a structured format, offer a reliable source of knowledge for reasoning. Nevertheless, existing KG-based LLM reasoning methods only treat KGs as factual knowledge bases and overlook the importance of their structural information for reasoning. In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning. Specifically, we present a planning-retrieval-reasoning framework, where RoG first generates relation paths grounded by KGs as faithful plans. These plans are then used to retrieve valid reasoning paths from the KGs for LLMs to conduct faithful reasoning. Furthermore, RoG not only distills knowledge from KGs to improve the reasoning ability of LLMs through training but also allows seamless integration with any arbitrary LLMs during inference. Extensive experiments on two benchmark KGQA datasets demonstrate that RoG achieves state-of-the-art performance on KG reasoning tasks and generates faithful and interpretable reasoning results.

Retrieve information that spans across multiple documents

Understanding data points through the context of their relationships

Our focus in this post is to extract information from unstructured text using LLMs