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Neural algorithmic reasoning focuses on building models that can execute classic algorithms. It allows one to combine the advantages of neural networks, such as handling raw and noisy input data, with theoretical guarantees and strong generalization of algorithms. Assuming we have a neural network capable of solving a classic algorithmic task, we can incorporate it into a more complex pipeline and train end-to-end. For instance, if we have a neural solver aligned to the shortest path problem, it can be used as a building block for a routing system that accounts for complex and dynamically changing traffic conditions. In our work [ref1], we study algorithmic reasoners trained only from input-output pairs, in contrast to current state-of-the-art approaches that utilize the trajectory of a given algorithm. We propose several architectural modifications and demonstrate how standard contrastive learning techniques can regularize intermediate computations of the models without appealing to any predefined algorithm’s trajectory.

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…

Nature Machine Intelligence - Deep learning is a powerful method to process large datasets, and shown to be useful in many scientific fields, but models are highly parameterized and there are often...

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.

The numerical representation of data is often ambiguous. This leads to a gauge theoretic view on data, requiring covariant or equivariant neural networks which are reviewed in this blog post.

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

In this article, we will talk about classical computation: the kind of computation typically found in an undergraduate Computer Science course on Algorithms and Data Structures [1]. Think shortest path-finding, sorting, clever ways to break problems down into simpler problems, incredible ways to organise data for efficient retrieval and updates.

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

At Neo4j, we developed a new graph format that is more IO and CPU efficient and handles low memory situations better.

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

MESH: A Flexible Distributed Hypergraph Processing System

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We deal with these things Transformer and Geometric graph improvement room

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…

Rdflib-neo4j introduces a client-side evolution of neosemantics, empowering data ingestion in all cloud and local Neo4j deployments

By request, here are the slides from our #neurips2023 presentation yesterday! We really enjoyed the opportunity to present the different aspects of the work… | 18 comments on LinkedIn

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…