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 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 Editorialfrontiersin.org

SHACLens: a visualization workflow for SHACL violation exploration in knowledge graphs

A visualization workflow for exploring SHACL violations in large knowledge graphs.

The paper presents SHACLens, an interactive visualization workflow that links ontology, instance data, and violation reports across multiple coordinated views. The workflow is designed to help analysts identify co-occurring errors and their likely upstream causes. An evaluation of the workflow using a transcriptomics dataset showed that it efficiently surfaced repeated sets of errors due to missing objects and schema inconsistencies.

Based on: Frontiers | SHACLens: a visualization workflow for SHACL violation exploration in knowledge graphs · frontiersin.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 Editorialfigureandground.substack.com

The Ontology Layer of Design

An article discussing the importance of ontology in design, particularly in the context of AI and large language models.

The author argues that designers must adapt to the changing landscape of AI by defining a product's ontology, which is the basic structure of objects, relationships, and concepts. This involves understanding how to spot good ontologies from bad ones and leveraging language as a way of shaping the worlds our new AI tools will inhabit.

Based on: The Ontology Layer of Design · figureandground.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

mcp-proto-okn: Natural-language access to open scientific knowledge graphs through the Model Context Protocol

A Python-based Model Context Protocol server for natural-language access to open scientific knowledge graphs.

The MCP Server Proto-OKN enables AI assistants to discover, inspect, and query scientific knowledge graphs through natural language. It provides various functions such as graph routing, schema inspection, and SPARQL execution. The server is implemented in Python using the FastMCP framework and is available on GitHub.

Based on: mcp-proto-okn: Natural-language access to open scientific knowledge graphs through the Model Context Protocol · arXiv

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

HighlightCurated by Aramai EditorialarXiv

Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge Graphs

A paper proposing a modular pipeline for building a travel-domain reasoning LLM grounded in an expert-designed knowledge graph.

The authors propose a modular pipeline to build a travel-domain reasoning large language model (LLM) using a domain-specific knowledge graph. The pipeline integrates a travel KG, bottom-up construction procedure, and supervised fine-tuning stage to embed domain knowledge into the LLM. The approach achieves high accuracy on a benchmark dataset.

Based on: Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge Graphs · arXiv

HighlightCurated by Aramai EditorialData & Knowledge Engineering

Semantic constraint validation in knowledge representation for the semantic web: A survey, taxonomy and research challenges

A survey, taxonomy and research challenges on semantic constraint validation.

This paper presents a survey and taxonomy of semantic constraint validation techniques for knowledge representation on the Semantic Web. It identifies research challenges and gaps in current approaches. The authors aim to provide a comprehensive overview of existing methods and their limitations.

Based on: Semantic constraint validation in knowledge representation for the semantic web: A survey, taxonomy and research challenges · Data & Knowledge Engineering

HighlightCurated by Aramai EditorialarXiv

Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models

A research paper exploring generalization in knowledge graph foundation models.

The authors investigate the ability of knowledge graph foundation models to generalize from partial links. They examine whether providing half a link is sufficient for accurate predictions. The study aims to understand how these models can be improved for more efficient and effective knowledge retrieval.

Based on: Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models · arXiv