As part of our multi-year collaboration with Liverpool FC, we develop a full AI system that can advise coaches on corner kicks

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…

🚀 Exciting News Alert! 🚀 We're over the moon to announce the launch of TigerGraph CoPilot's public alpha release! 🌟 🔗 Get ready to explore the future of…

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.

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

In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this move presents significant challenges for personalized recommendations. Unlike music and podcasts, audiobooks, initially available for a fee, cannot be easily skimmed before purchase, posing higher stakes for the relevance of recommendations. Furthermore, introducing a new content type into an existing platform confronts extreme data sparsity, as most users are unfamiliar with this new content type. Lastly, recommending content to millions of users requires the model to react fast and be scalable. To address these challenges, we leverage podcast and music user preferences and introduce 2T-HGNN, a scalable recommendation system comprising Heterogeneous Graph Neural Networks (HGNNs) and a Two Tower (2T) model. This novel approach uncovers nuanced item relationships while ensuring low latency and complexity. We decouple users from the HGNN graph and propose an innovative multi-link neighbor sampler. These choices, together with the 2T component, significantly reduce the complexity of the HGNN model. Empirical evaluations involving millions of users show significant improvement in the quality of personalized recommendations, resulting in a +46% increase in new audiobooks start rate and a +23% boost in streaming rates. Intriguingly, our model's impact extends beyond audiobooks, benefiting established products like podcasts.

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

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…

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

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…

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…

Telecom GenAI based Network Operations: The Integration of LLMs, GraphRAG, Reinforcement Learning, and Scoring Models With the increasing complexity of… | 12 comments on LinkedIn

LLM frameworks like LangChain are moving towards a graph-based approach for handling their workflows. This represents the initial steps of a much larger… | 90 comments on LinkedIn

Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world networks. Despite these advancements, there remain considerable challenges in exploring more realistic systems and enhancing practical applications. The emergence of artificial intelligence (AI) technologies, coupled with the abundance of diverse real-world network data, has heralded a new era in complex network science research. This survey aims to systematically address the potential advantages of AI in overcoming the lingering challenges of complex network research. It endeavors to summarize the pivotal research problems and provide an exhaustive review of the corresponding methodologies and applications. Through this comprehensive survey-the first of its kind on AI for complex networks-we expect to provide valuable insights that will drive further research and advancement in this interdisciplinary field.

Writer CEO May Habib says its semantic graphing approach is an alternative to the chunking process of RAG using vector databases.

Relational Harmony and a New Hope for Dimensionality ⛓️ In an era where data complexity escalates and the quest for meaningful technology integration… | 30 comments on LinkedIn

Achieving Robust Reasoning by Combining Neural and Symbolic Representations 🌗 Recent breakthroughs in large language models (LLMs) demonstrate impressive…

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.

Translating visual networks into graphs using AI is a game changer for accelerating analytics on information that is historically difficult to access.

Check out this chatbot (https://lnkd.in/gv8Afk57) that I created entirely based on the prolific writings of Mike Dillinger, PhD . This chatbot helps you better…

"It is possible to build KGs with LMs through prompt engineering. But are we boiling the ocean? Can we improve the quality of the generated graph elements by using - dare I say it - SLMs (small language models)"

Over the past 5 years, news about AI has been filled with amazing research – at first focused on graph neural networks (GNNs) and more recently about large language models (LLMs). Understand that business tends to use connected data – networks, graphs – whether you’re untangling supply networks in Manufacturing, working on drug discovery for Pharma, or mitigating fraud in Finance. Starting from supplier agreements, bill of materials, internal process docs, sales contracts, etc., there’s a graph inside nearly every business process, one that is defined by language. This talk addresses how to leverage both natural language and graph technologies together for AI applications in industry. We’ll look at how LLMs get used to build and augment graphs, and conversely how graph data gets used to ground LLMs for generative AI use cases in industry – where a kind of “virtuous cycle” is emerging for feedback loops based on graph data. Our team has been engaged, on the one hand, with enterprise use cases in manufacturing. On the other hand we’ve worked as intermediaries between research teams funded by enterprise and open source projects needed by enterprise – particularly in the open source ecosystem for AI models. Also, there are caveats; this work is not simple. Translating from latest research into production-ready code is especially complex and expensive. Let’s examine caveats which other teams should understand, and look toward practical examples.