GraphNews

Read the joint letter from Neo4j and AWS to graph customers, the graph curious, and the Cypher community on the new GQL standard.

Explore Altair's latest news releases for cutting-edge innovations, collaborations, and industry insights driving technological advancements and progress.

Standards body adoption could help ease portability between vendors

Free diagram visualization extension for JupyterLab and Jupyter Notebook.

Ultipa Graph, one of the newer entrants to the graph database space, is approaching its 5.0 version update.

AI has been a part of healthcare and life sciences for decades. You might remember hearing about the very first chatbot, ELIZA, created at MIT in 1964 by Joseph Weizenbaum to explore communication between machines and humans.

💥 Breaking News: #ECLASS as #RDF is now a reality! 😎 🎉 By leveraging RDF serialization, ECLASS is now poised to revolutionize #data #interoperability and…

Interested in the world of semantic web? Then maybe you stumbled on the right article this time. I will be talking about the…

📢 How to apply Graph Neural Networks to stock predictions? The following paper presents the ideal starting point ….keep reading! 👇 This paper proposes the…

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...

Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential for effective resource allocation in such environments. In this paper, we introduce the Spatial-Temporal Graph Encoder-Decoder (STGED) framework for Tactical Communication Networks that leverages both spatial and temporal features of network states to learn latent tactical behaviors effectively. STGED hierarchically utilizes graph-based attention mechanism to spatially encode a series of communication network states, leverages a recurrent neural network to temporally encode the evolution of states, and a fully-connected feed-forward network to decode the connectivity in the future state. Through extensive experiments, we demonstrate that STGED consistently outperforms baseline models by large margins across different time-steps input, achieving an accuracy of up to 99.2\% for the future state prediction task of tactical communication networks.

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.

The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it. The primary focus of their research is to ensure safety, comfort, and efficiency. An autonomous vehicle has several modules responsible for one or more of the aforementioned items. Among these modules, the trajectory planner plays a pivotal role in the safety of the vehicle and the comfort of its passengers. The module is also responsible for respecting kinematic constraints and any applicable road constraints. In this paper, a novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories. First, a spatial-temporal graph is constructed using the autonomous vehicle, its surrounding vehicles, and virtual nodes along the road with respect to the vehicle itself. Next, the graph is forwarded into a sequential network to obtain the desired states. To support the planner, a simple behavioral layer is also presented that determines kinematic constraints for the planner. Furthermore, a novel potential function is also proposed to train the network. Finally, the proposed planner is tested on three different complex driving tasks, and the performance is compared with two frequently used methods. The results show that the proposed planner generates safe and feasible trajectories while achieving similar or longer distances in the forward direction and comparable comfort ride.

Accurate traffic forecasting is essential for effective urban planning and congestion management. Deep learning (DL) approaches have gained colossal success in traffic forecasting but still face challenges in capturing the intricacies of traffic dynamics. In this paper, we identify and address this challenges by emphasizing that spatial features are inherently dynamic and change over time. A novel in-depth feature representation, called Dynamic Spatio-Temporal (Dyn-ST) features, is introduced, which encapsulates spatial characteristics across varying times. Moreover, a Dynamic Spatio-Temporal Graph Transformer Network (DST-GTN) is proposed by capturing Dyn-ST features and other dynamic adjacency relations between intersections. The DST-GTN can model dynamic ST relationships between nodes accurately and refine the representation of global and local ST characteristics by adopting adaptive weights in low-pass and all-pass filters, enabling the extraction of Dyn-ST features from traffic time-series data. Through numerical experiments on public datasets, the DST-GTN achieves state-of-the-art performance for a range of traffic forecasting tasks and demonstrates enhanced stability.

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.

Latest findings in multiple research directions for handling graph construction and network security issues

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

Motivation and context

Built upon the shoulders of graph theory, the field of complex networks has become a central tool for studying real systems across various fields of resear

🧠 Visualizing Automatically Generated AI Semantic Networks🧠 Yesterday I shared an #automation that leverages #Claude3 in an iterative process to create… | 17 comments on LinkedIn

Exciting times for query languages as SC 32 publishes 39075 on GQL, read the highlights article here.