Learn how the 2023 Gartner Hype Cycle™ identifies exciting innovations in artificial intelligence that will be beneficial to business.

Our paper "𝐺𝑟𝑎𝑝ℎ𝐸𝑅: 𝐴 𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒-𝑎𝑤𝑎𝑟𝑒 𝑇𝑒𝑥𝑡-𝑡𝑜-𝐺𝑟𝑎𝑝ℎ 𝑀𝑜𝑑𝑒𝑙 𝑓𝑜𝑟 𝐸𝑛𝑡𝑖𝑡𝑦 𝑎𝑛𝑑 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛… | 34 comments on LinkedIn

Learn how to use Neo4j and Senzing to build entity resolved knowledge graphs that remove duplicate data and improve analytics accuracy.

A survey of the current methods of integration

cognee: Train your knowledge graph generation and search with DSPy, Weaviate, and Neo4j to generate deterministic LLMs outputs! GitHub…

Transform plain text into a visually stunning Knowledge Graph with GPT-4 (latest preview)! It converts text into RDF tuples, and highlights the most frequent connections with a vibrant color-coding...

Recent work demonstrated great promise in the idea of orchestrating collaborations between LLMs, human input, and various tools to address the inherent limitations of LLMs. We propose a novel perspective called semantic decoding, which frames these collaborative processes as optimization procedures in semantic space. Specifically, we conceptualize LLMs as semantic processors that manipulate meaningful pieces of information that we call semantic tokens (known thoughts). LLMs are among a large pool of other semantic processors, including humans and tools, such as search engines or code executors. Collectively, semantic processors engage in dynamic exchanges of semantic tokens to progressively construct high-utility outputs. We refer to these orchestrated interactions among semantic processors, optimizing and searching in semantic space, as semantic decoding algorithms. This concept draws a direct parallel to the well-studied problem of syntactic decoding, which involves crafting algorithms to best exploit auto-regressive language models for extracting high-utility sequences of syntactic tokens. By focusing on the semantic level and disregarding syntactic details, we gain a fresh perspective on the engineering of AI systems, enabling us to imagine systems with much greater complexity and capabilities. In this position paper, we formalize the transition from syntactic to semantic tokens as well as the analogy between syntactic and semantic decoding. Subsequently, we explore the possibilities of optimizing within the space of semantic tokens via semantic decoding algorithms. We conclude with a list of research opportunities and questions arising from this fresh perspective. The semantic decoding perspective offers a powerful abstraction for search and optimization directly in the space of meaningful concepts, with semantic tokens as the fundamental units of a new type of computation.

Neurosymbolic AI is an increasingly active area of research that combines symbolic reasoning methods with deep learning to leverage their complementary benefits. As knowledge graphs are becoming a popular way to represent heterogeneous and multi-relational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm. Traditionally, such approaches have utilized either rule-based inference or generated representative numerical embeddings from which patterns could be extracted. However, several recent studies have attempted to bridge this dichotomy to generate models that facilitate interpretability, maintain competitive performance, and integrate expert knowledge. Therefore, we survey methods that perform neurosymbolic reasoning tasks on knowledge graphs and propose a novel taxonomy by which we can classify them. Specifically, we propose three major categories: (1) logically-informed embedding approaches, (2) embedding approaches with logical constraints, and (3) rule learning approaches. Alongside the taxonomy, we provide a tabular overview of the approaches and links to their source code, if available, for more direct comparison. Finally, we discuss the unique characteristics and limitations of these methods, then propose several prospective directions toward which this field of research could evolve.

I really don't like dictionary-style definitions, as you may have noticed from previous posts. They're simply not informative enough for the mission-critical "keystone" concepts that we need to work with, concepts like customer, sale, skill, or knowledge graph.

Life Sciences data expert Dr Alexander Jarasch on the learnings other R&D teams can gain from the use of graphs at the Institut de Recherches Servier for our latest EPM Online Insight.

Buildings account for a substantial portion of global energy consumption. Reducing buildings' energy usage primarily involves obtaining data from building systems and environment, which are instrumental in assessing and optimizing the building's performance. However, as devices from various manufacturers represent their data in unique ways, this disparity introduces challenges for semantic interoperability and creates obstacles in developing scalable building applications. This survey explores the leading semantic modeling techniques deployed for energy management in buildings. Furthermore, it aims to offer tangible use cases for applying semantic models, shedding light on the pivotal concepts and limitations intrinsic to each model. Our findings will assist researchers in discerning the appropriate circumstances and methodologies for employing these models in various use cases.

Integrating large language models (LLMs) with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As these models grow more capable, it is crucial to enable them to perform multi-step inferences over real-world knowledge graphs while minimizing hallucination. While large language models excel at conversation and text generation, their ability to reason over domain-specialized graphs of interconnected entities remains limited. For example, can we query a LLM to identify the optimal contact in a professional network for a specific goal, based on relationships and attributes in a private database? The answer is no--such capabilities lie beyond current methods. However, this question underscores a critical technical gap that must be addressed. Many high-value applications in areas such as science, security, and e-commerce rely on proprietary knowledge graphs encoding unique structures, relationships, and logical constraints. We introduce a fine-tuning framework for developing Graph-aligned LAnguage Models (GLaM) that transforms a knowledge graph into an alternate text representation with labeled question-answer pairs. We demonstrate that grounding the models in specific graph-based knowledge expands the models' capacity for structure-based reasoning. Our methodology leverages the large-language model's generative capabilities to create the dataset and proposes an efficient alternate to retrieval-augmented generation styled methods.

A comparison of FalkorDB with other Knowledge Graphs for developing AI applications with.

Motivation and context

Sorry to those immersed in DSPy, but this might be the least magical DSPy RAG (retrieval-augmented generation) pipeline you’ve ever seen. The DSPy… | 29 comments on LinkedIn

Copyright 2024 Kurt Cagle / The Cagle Report There is an interesting tug-of-war going on right now. On one side are the machine learning folks, those who have been harnessing neural networks for a few years.

The Iterative Dance of the RAG Framework for Knowledge-Grounded Language AI ⭕ To truly unlock the potential of LLMs for open-ended question answering and…

The distinction between types and individuals is key to most conceptual modeling techniques and knowledge representation languages. Despite that, there are a number of situations in which modelers navigate this distinction inadequately, leading to pr

One blocker to more widespread deployment of knowledge graphs in AI is the belief that they are inherently a discrete form of knowledge…

A few years ago we started the "RDF community" to create a vendor-neutral place to discuss RDF, Knowledge Graphs, Semantic Web and Linked Data. I've finally…

The Rise of Graph Jobs, The Disappearance of Graph Technology? Join me for this fun presentation at KGC on May 8, 2024, in NYC, where I delve into a "State of…

This new era of « Active MetaData Era» could have been named GO Era for « Graphs & Ontologies ». Active metadata refers to metadata that is dynamic and…

Scientific Data - An open source knowledge graph ecosystem for the life sciences

The W3C RDF-star WG is currently debating a design issue which may greatly affect our chances of having a better alignment between RDF graphs and Labeled… | 13 comments on LinkedIn

AI Answers Questions on Any Knowledge Graph - No Training Required ... Imagine being able to ask complex questions about any knowledge graph and get accurate…

Entity resolved knowledge graphs result from applying entity resolution technology to graph data, resulting in graphs with much more utility.

A straightforward introduction to integrating language models (LLMs) with knowledge graphs (KGs) for enhanced intelligence.

In response to my last blog, LLMs and Knowledge — the study continues, I got a lot of questions LinkedIn, which I decided to answer in…

A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain

Learn more about what graph embeddings are and how they're used to accelerate real-time analytics. Find out about graph embedding algorithms and storage.