knowledge-graphs

Introduces NL2SHACL-Bench, a benchmark suite for translating natural language requirements into SHACL shapes, evaluating four state-of-the-art LLMs and showing they struggle with semantic equivalence for complex patterns.

A technical post explaining why RAG fails for multi-hop queries and presenting a 9-step roadmap for building context graphs, where entities are nodes and relationships are edges, with a minimal 150-line engine.

KnowPlan is an extraction-first framework for personalized academic degree pathway planning, using CatalogBrowse to reconstruct curricula from heterogeneous university sources and DegreeMap to optimize personalized plans via CP-SAT, achieving high recall and feasibility across evaluations.

This paper proposes the Mecellem semantic protocol, an ontologically grounded framework for artificial legal intelligence, arguing that legal reasoning requires dynamic, context-dependent meaning construction rather than mere codification or statistical pattern recognition.

ProPRL introduces a property-aware framework for prerequisite relation learning in educational knowledge graphs, combining concept-resource hypergraph and directed behavior graph with adaptive pair-conditioned fusion and an irreversibility constraint to achieve state-of-the-art performance.

PULSE is a new executable contract language for spatiotemporal knowledge graph engineering, providing a typed runtime with role-based write effects, safety properties verified in Lean 4, and trace parity across large datasets.

This paper presents a methodology for integrating knowledge graphs into ROS 2-based robotic systems to improve autonomy and decision-making in missions like search and rescue, demonstrated with the Aerostack2 framework.

IRIS is a training-free framework that uses frozen large language models to construct reusable identity representations for entities in knowledge graphs, enabling efficient entity alignment across different KGs without pair-dependent processing.

HyCE-RAG is a novel hypergraph-based retrieval-augmented generation framework for multi-hop question answering that constructs explicit evidence chains via confidence-aware heuristic search, outperforming standard RAG and graph-based RAG methods in accuracy, relevance, and faithfulness.

IBM has launched a free 1-hour course on building agentic knowledge graphs, covering topics from introduction to multi-agent orchestration.

MA-DAR is a plug-and-play framework that addresses representation conflicts in replay-based continual temporal knowledge graph reasoning by aligning replayed and current representations on a shared manifold and using a dynamic gating mechanism for adaptive fusion.

A practical guide to building AI agents using LLMs, RAG, and knowledge graphs, available on Amazon.

Benchmarks of 10 graph serialization formats reveal that verbose formats waste tokens, while tabular layouts improve accuracy; the author built ISONGraph, a property-graph format optimized for LLM comprehension with 70% fewer tokens and MIT licensing.

The article argues that as AI agents proliferate, a new layer of the internet based on knowledge graphs will emerge, reviving Tim Berners-Lee's Semantic Web concept to provide structured highways for agent interaction and reasoning.

Promotion of the book 'Graph Algorithms for Data Science' which teaches graph algorithms and their applications using Neo4j, covering topics like knowledge graphs, social network analysis, and node embeddings.

Promotional announcement for the book 'Digital Twins in Action' by Greg Biegel, covering digital twin creation, IoT, AI, and knowledge graphs, available for pre-order on Amazon.

ProbSPARQL is an upward-compatible SPARQL extension that models uncertain numeric values as random variables with probabilistic RDF literal datatypes, enabling distribution-aware queries, probabilistic filters, and divergence-based joins. Implemented on Apache Jena ARQ, it addresses challenges in querying multi-dimensional uncertain measurement data from circular manufacturing knowledge graphs.

Introduces Generative Ontology Induction (GOI), a domain-agnostic framework that uses LLMs to induce structured ontologies from document corpora, achieving high coverage across diverse domains.

This survey comprehensively reviews GNN-based link prediction from a dedicated GNN perspective, categorizing recent advancements by techniques (GCN, GAE, GAT, GFormer) and applications (knowledge graphs, recommendation systems), and discusses challenges and future directions.

A summary of research comparing agent-memory solutions from Cognee, Graphiti, and Neo4j, which all use knowledge graphs and vector search with LLM-based data extraction.