#knowledge graphs

14 results
cs.DB2026

Fully Inductive Cardinality Estimation

Tim Schwabe, Lukas Ketzer, Maribel Acosta

The paper introduces FICE, a graph neural network‑based estimator that can predict the cardinalities of SPARQL Basic Graph Pattern queries on knowledge graphs it has never seen bef…

#cardinality estimation#graph neural networks#knowledge graphs#sparql query optimization
math.OC2026

OptGraph: Large Language Models Enhanced Evolutionary Optimization Via Graph Retrieval-Augmented Generation

Xianchao Xiu, Jianhao Li, Huangyue Chen +1

The paper introduces OptGraph, a system that enhances automated evolutionary optimization by using large language models together with a graph‑based retrieval‑augmented generation…

#evolutionary optimization#large language models#graph retrieval#knowledge graphs
cs.AI2026

Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

Bente Gortworst, Cem Okulmus, Magdalena Ortiz +1

The paper studies automatic learning of SHACL shapes for knowledge graph validation by fitting positive and negative example nodes, focusing on a core SHACL fragment equivalent to…

#shacl#shape learning#knowledge graphs#description logic
cs.AI2026

GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation

Maya Arseven, Anette Frank, Beni Egressy +2

The paper introduces a graph language model (GLM) based retriever for retrieval-augmented generation over knowledge graphs and compares it with GNN‑based and vector‑search retrieve…

#graph language models#retrieval-augmented generation#knowledge graphs#multi-hop reasoning
cs.CL2026

GGC: Selective Query Correction for Reliable Text-to-SPARQL Generation

Ziyi Yang, Thanh-Son Nguyen, Tuan Anh Nguyen +1

The paper introduces GGC, a framework that lets a language model generate a SPARQL query, predicts if it needs fixing, and only corrects high‑risk cases, boosting accuracy and cutt…

#text-to-sparql#query generation#selective correction#large language models
cs.DL2026

SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions

Jennifer D'Souza, Sameer Sadruddin, Anisa Rula +23

The paper introduces SciSchema.org, a multidisciplinary collection of 16 expert‑annotated schemas for describing scientific processes in a structured way, created using a human‑in‑…

#scientific process modeling#multidisciplinary schemas#knowledge graphs#semantic publishing
cs.DB2026

AuthentiCity: A Multi-Source Provenance-Aware Knowledge Graph and Benchmark for 3D City Models

Huynh Duc An Son Nguyen, Lukas Arzoumanidis, Youness Dehbi

AuthentiCity is a large, provenance‑aware knowledge graph that integrates authoritative, crowd‑sourced, and machine‑learned data for 3D city models across five global cities, enabl…

#urban digital twins#knowledge graphs#provenance tracking#3d city models
cs.CL2026

IRIS: Reusable Identity Representations from Frozen LLMs for Entity Alignment

Xinran Liu, Shengtao Li, Shouqian Shi +2

The paper introduces IRIS, a training-free method that uses frozen large language models to generate stable identity signatures for entities, enabling direct similarity-based align…

#entity alignment#knowledge graphs#large language models#representation learning
cs.CL2026

Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs

Fanfu Wei, Thibault Ehrhart, Raphaël Troncy

The paper introduces Kontrast, a framework that automatically detects and categorizes inconsistencies between textual information, tables, and knowledge graphs by converting text t…

#knowledge consistency#multimodal data#tables#knowledge graphs
cs.AI2026

MedBeads: An AI-Native Clinical Context Graph Built from Immutable Beads and Reconstructable Clinical Links

Takahito Nakajima

The paper presents MedBeads, an AI‑native clinical context graph that stores immutable, hash‑identified clinical objects (Beads) in an append‑only, patient‑scoped structure and der…

#clinical data management#knowledge graphs#immutable data structures#fhir interoperability
cs.DL2026

Towards a Bridge Layer Between Bibliographic and Formalized Mathematical Knowledge

A. Mayeux

The paper proposes a relational bridge database that links bibliographic metadata with formal proof libraries and introduces a formalization score to estimate how much of a publica…

#bibliographic databases#formal proof libraries#knowledge graphs#formalization score
cs.LG2026

NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis

Lincan Li, Zheng Chen, Yushun Dong

NeuroGRIP is a framework that refines EEG-based graph neural network predictions for seizure diagnosis by retrieving and integrating clinical knowledge from a domain-specific knowl…

#eeg seizure detection#graph neural networks#knowledge graphs#retrieval-augmented reasoning
cs.DL2026

Knowledge Tectonics: A Geodynamic Inspired Framework for Modeling Epistemic Changes

Thomas Van Erven, Stelios Kontogiannis, Efstratios Kontopoulos +3

The paper proposes a geodynamic-inspired framework called Knowledge Tectonics to model and visualize how concepts and metadata evolve over time in semantic web knowledge graphs, de…

#knowledge dynamics#semantic web#knowledge graphs#visual analytics
cs.IR2026

FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis

Marlena Flüh, Soo-Yon Kim, Carolin Victoria Schneider +1

The paper presents FAIR GraphRAG, a framework that combines Retrieval‑Augmented Generation with FAIR Digital Objects to enable graph‑based retrieval and question answering over bio…

#retrieval-augmented generation#fair principles#knowledge graphs#biomedical data