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Semantic Web and Labeled Property Graphs have their use-cases, but complex, real-time business operations is not one of them

A semantic layer for end-to-end, intelligent automation

And this is the amazing prof. Maurizio Lenzerini on the benefits of using ontologies for data integration, captured when showing the long journey of knowledge…

Unlocking the Potential of Large Language Models for Graph Learning … The astounding capabilities of Large Language Models continue to push the boundaries of…

Leveraging LLMs for Causal Reasoning: Why Knowledge and Algorithms are Key 🌳 Causal reasoning — the capacity to understand cause-effect relationships and…

𝗟𝗲𝘁 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝗽𝗲𝗮𝗸! Inject structured data directly with GraphTokens and supercharge your LLM's reasoning abilities. Our exciting research is… | 16 comments on LinkedIn

Ontologies and Knowledge Graphs offer a way to connect embedding vectors to structured knowledge, enhancing their meaning and explainability. Let's delve into… | 25 comments on LinkedIn

Information developers, technical writers, and knowledge management professionals face enormous challenges that are often not clear to their "customers", and not even to their managers. In a nutshell, they put significant effort into organizing and managing enormous collections of information – in t

A Step-by-step Walkthrough with GenAI-Stack and OpenAI

This article gives a practical introduction to the potential of causal graphs.

We explore the 3 types of recursive retrieval — Page-Based, Information-Centric, and Concept-Centric retrieval

There is no intelligence without knowledge. Despite the incredible power of Large Language Models (LLM), they still significantly struggle…

Ontologies are the backbone of the Semantic Web bridging the gap between human and machine understanding. They define the concepts and relationships that… | 29 comments on LinkedIn

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 research. Represented as graphs, different systems can be studied using the same analysis methods, which allows for their comparison. Here, we challenge the wide-spread idea that graph theory is a universal analysis tool, uniformly applicable to any kind of network data. Instead, we show that many classical graph metrics (including degree, clustering coefficient and geodesic distance) arise from a common hidden propagation model: the discrete cascade. From this perspective, graph metrics are no longer regarded as combinatorial measures of the graph, but as spatio-temporal properties of the network dynamics unfolded at different temporal scales. Once graph theory is seen as a model-based (and not a purely data-driven) analysis tool, we can freely or intentionally replace the discrete cascade by other canonical propagation models and define new network metrics. This opens the opportunity to design, explicitly and transparently, dedicated analyses for different types of real networks by choosing a propagation model that matches their individual constraints. In this way, we take stand that network topology cannot always be abstracted independently from network dynamics, but shall be jointly studied. Which is key for the interpretability of the analyses. The model-based perspective here proposed serves to integrate into a common context both the classical graph analysis and the more recent network metrics defined in the literature which were, directly or indirectly, inspired by propagation phenomena on networks.

How to Combine RAG, Reinforcement Learning, and Knowledge Graphs for More Robust AI Agents ⭕ Recent months have seen rapid progress in developing large…

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH (LZI), Wadern

With this demo, we wanted to demonstrate 3 key powerful applications of knowledge graph in RAG, and demonstrate how it can improve RAG…

Leveraging Large Language Models to Assemble Knowledge Graphs Grounded in Reality 🔺 The exponential growth of data has brought both vast opportunities and… | 17 comments on LinkedIn

Several scientists in the first half of the 20th century made enormous contributions to science and technology in different fields. We can mention sever...

Ontologies are traditionally expressed in the Web Ontology Language (OWL), that provides a syntax for expressing taxonomies with axioms regulating class membership. The semantics of OWL, based on Description Logic (DL), allows for the use of automated reasoning to check the consistency of ontologies, perform classification, and to answer DL queries. However, the open world assumption of OWL, along with limitations in its expressiveness, makes OWL less suitable for modelling rules and regulations, used in public administration. In such cases, it is desirable to have closed world semantics and a rule-based engine to check compliance with regulations. In this paper we describe and discuss data model management using the Shapes Constraint Language (SHACL), for concept modelling of concrete requirements in regulation documents within the public sector. We show how complex regulations, often containing a number of alternative requirements, can be expressed as constraints, and the utility of SHACL engines in verification of instance data against the SHACL model. We discuss benefits of modelling with SHACL, compared to OWL, and demonstrate the maintainability of the SHACL model by domain experts without prior knowledge of ontology management.

Entity alignment, which is a prerequisite for creating a more comprehensive Knowledge Graph (KG), involves pinpointing equivalent entities across disparate KGs. Contemporary methods for entity alignment have predominantly utilized knowledge embedding models to procure entity embeddings that encapsulate various similarities-structural, relational, and attributive. These embeddings are then integrated through attention-based information fusion mechanisms. Despite this progress, effectively harnessing multifaceted information remains challenging due to inherent heterogeneity. Moreover, while Large Language Models (LLMs) have exhibited exceptional performance across diverse downstream tasks by implicitly capturing entity semantics, this implicit knowledge has yet to be exploited for entity alignment. In this study, we propose a Large Language Model-enhanced Entity Alignment framework (LLMEA), integrating structural knowledge from KGs with semantic knowledge from LLMs to enhance entity alignment. Specifically, LLMEA identifies candidate alignments for a given entity by considering both embedding similarities between entities across KGs and edit distances to a virtual equivalent entity. It then engages an LLM iteratively, posing multiple multi-choice questions to draw upon the LLM's inference capability. The final prediction of the equivalent entity is derived from the LLM's output. Experiments conducted on three public datasets reveal that LLMEA surpasses leading baseline models. Additional ablation studies underscore the efficacy of our proposed framework.

The Intersection of Graphs and Language Models 🔲 ⚫ Large language models (LLMs) have rapidly advanced, displaying impressive abilities in comprehending… | 27 comments on LinkedIn

Links * Python Examples * JS Examples * YouTube Last week we highlighted LangGraph - a new package (available in both Python and JS) to better enable creation of LLM workflows containing cycles, which are a critical component of most agent runtimes. As a part of the launch, we highlighted two simple runtimes:

⚡ Building language agents as graphs ⚡

Whether we remember them or not, we rely directly on unexamined and often very murky foundational assumptions that permeate everything we do. These assumptions are formulated using keystone concepts – core concepts that are so crucial that mere dictionary-style definitions are not enough.