HighlightCurated by Aramai EditorialarXiv

Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!

Paper on improving question answering systems with large language models using ontologies.

This paper presents an approach to improve the accuracy of question answering systems with large language models by leveraging ontologies. The authors propose a method that consists of ontology-based query check and LLM repair, which increases the overall accuracy to 72%. The results provide further evidence that investing knowledge graphs, namely the ontology, provides higher accuracy for LLM-powered question-answering systems.

Based on: Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue! · arxiv.org

HighlightCurated by Aramai Editorialgit.nextgraph.org

oxigraph

A Rust-based graph database implementing the SPARQL standard.

Oxigraph is a graph database written in Rust that implements the SPARQL standard. It provides a compliant, safe, and fast graph database based on the RocksDB key-value store. Oxigraph also includes utility functions for reading, writing, and processing RDF files.

Based on: oxigraph · git.nextgraph.org

HighlightCurated by Aramai Editorialw3.org

SHACL 1.2 Rules

This document defines SHACL Rules, a language for describing the structure of RDF graphs.

SHACL 1.2 Rules is a specification that defines a language for describing the structure of RDF graphs and provides inferencing with the generation of new RDF data from a combination of rules and a base data graph. The document defines the syntax and semantics of rule-based inference, including basic patterns, recursion, filtering, negation, assignment, and importing rules. It also covers the evaluation of a rule set and the relationship between SHACL Rules and SPARQL.

Based on: SHACL 1.2 Rules · w3.org

HighlightCurated by Aramai Editorialoaei.ontologymatching.org

Ontology Alignment Evaluation Initiative::2025

Evaluation campaign for ontology matching technologies.

The Ontology Alignment Evaluation Initiative (OAEI) is a yearly evaluation campaign that assesses the performance of ontology matching systems. The 2025 campaign includes various tracks, such as T-Box/Schema matching, Multifarm, and Knowledge Graph Track, which evaluate different aspects of ontology alignment. The goal is to provide a comprehensive assessment of ontology matching technologies.

Based on: Ontology Alignment Evaluation Initiative::2025 · oaei.ontologymatching.org

HighlightCurated by Aramai Editorialjessicatalisman.substack.com

Intentional Arrangement

A Substack publication by Jessica Talisman, MLS, on information architecture and semantic engineering.

The resource covers topics such as ontologies, knowledge graphs, AI, and semantic interoperability. It includes essays and articles on various aspects of digital knowledge ecosystems and their organization. The author shares her expertise in information architecture and semantic engineering, with a focus on intentional arrangement and its applications.

Based on: Intentional Arrangement | Jessica Talisman, MLS | Substack · jessicatalisman.substack.com

HighlightCurated by Aramai Editorialontologist.substack.com

Converting from OWL to SHACL, Part I

An article discussing the principles and benefits of converting from OWL to SHACL.

The author explores the reasons for converting from OWL to SHACL, including improved validation, support for reification, and better alignment with tabular data sources. The article also delves into design considerations, such as the differences between rdfs:subClassOf and sh:node in a NodeShape.

Based on: Converting from OWL to SHACL, Part I · ontologist.substack.com

HighlightCurated by Aramai EditorialarXiv

Skill Retrieval Augmentation for Agentic AI

A paper proposing a paradigm for dynamically retrieving and incorporating skills in large language models.

The authors introduce Skill Retrieval Augmentation (SRA), a method for agents to retrieve relevant skills from external corpora on demand. They construct a benchmark, SRA-Bench, to evaluate the full SRA pipeline. The paper shows that retrieval-based skill augmentation can improve agent performance and highlights the need for more efficient skill incorporation.

Based on: Skill Retrieval Augmentation for Agentic AI · arXiv

HighlightCurated by Aramai EditorialarXiv

Separating Semantic Competition from Context Length in RAG Reading

Paper on evaluating Retrieval-Augmented Generation (RAG) systems.

The authors introduce a matched-control protocol to isolate the effect of semantic competition on RAG reading performance. They apply this protocol to two compact open models on SQuAD and report improvements in F1, answer inclusion, and exact match scores. The results suggest that the competition effect is distinct from context length.

Based on: Separating Semantic Competition from Context Length in RAG Reading · arXiv

HighlightCurated by Aramai EditorialarXiv

CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

A framework for improving the reliability of reasoning language models through error checking and correction.

CheckRLM proposes a framework to improve the reliability of reasoning language models by timely checking and correcting factual errors. It extracts claims from the reasoning chain, identifies inconsistencies, and performs minimal-cost corrections using external knowledge. The framework demonstrates strong capability in mitigating error accumulation in long-horizon reasoning with lower costs.

Based on: CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning · arXiv

HighlightCurated by Aramai EditorialIEEE Communications Magazine

Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication

A framework for efficiently and verifiably updating large language model-enabled semantic knowledge bases.

The authors propose a proof-of-unlearning framework for updating large language models (LLMs) used in semantic knowledge bases. The framework tracks the evolution of unlearning by measuring drifts in the LoRA adapter subspace. Experimental results demonstrate its effectiveness. This work addresses the challenge of removing outdated, malicious, or privacy-sensitive content from LLMs without retraining.

Based on: Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication · IEEE Communications Magazine

HighlightCurated by Aramai EditorialProceedings of the AAAI Conference on Artificial Intelligence

TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs

A framework for resolving factual conflicts between LLMs' internal knowledge and external information using knowledge graphs.

This paper proposes TruthfulRAG, a framework that leverages knowledge graphs to resolve factual conflicts in RAG systems. It constructs KGs from retrieved content, identifies relevant knowledge through query-based graph retrieval, and employs entropy-based filtering mechanisms to mitigate inconsistencies. The authors claim that TruthfulRAG outperforms existing methods in resolving knowledge conflicts and improving the robustness of RAG systems.

Based on: TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs · Proceedings of the AAAI Conference on Artificial Intelligence

HighlightCurated by Aramai EditorialarXiv

Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models

A paper proposing a framework for automating cause-and-effect logic generation using knowledge graphs and large language models.

The authors present a semantic-AI framework that combines a knowledge graph with a constrained large language model to automate the generation of cause-and-effect logic. The framework builds on an established modular alignment ontology and demonstrates its application on a modular process plant. This approach aims to reduce manual effort in creating engineering specifications.

Based on: Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models · arXiv