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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

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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

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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

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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

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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

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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

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BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration

A paper proposing a method for bootstrapping e-commerce attribute taxonomies using human-AI collaboration.

The authors present BEATS, an approach to iteratively refine e-commerce attribute taxonomies through human-AI collaboration. This method aims to improve search performance by leveraging AI-driven suggestions and human feedback. The proposed framework is designed to be applicable in various e-commerce scenarios.

Based on: BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration · arXiv

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Transforming SHACL Shape Graphs into HTML Applications for Populating Knowledge Graphs

A paper proposing a method to design user interfaces for populating knowledge graphs using SHACL constraint files.

The authors present an approach to create multi-form web applications from SHACL constraints, enabling user interface modeling and leveraging OWL reasoning for logical consistency. This method treats editing knowledge graphs as a business process, integrating ontology-based components. The application models are themselves knowledge graphs that can be verified using OWL reasoning.

Based on: Transforming SHACL Shape Graphs into HTML Applications for Populating Knowledge Graphs · Digital

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Translating and Formalizing the MIRAGE Guidelines to a Prototype MIRAGE Ontology and DCAT3 Extension Vocabulary for Glycomics Data Management

A paper on formalizing MIRAGE guidelines into a prototype ontology and DCAT3 extension vocabulary for glycomics data management.

The authors present a comprehensive semantic formalization of MIRAGE guidelines using an integrated RDF ontology framework. The framework models glycan structures, biological specimens, analytical instruments, and experimental processes with formal OWL semantics and SHACL validation constraints. It enables automated quality assessment, federated data querying, and enhanced reproducibility in glycomics research.

Based on: Translating and Formalizing the MIRAGE Guidelines to a Prototype MIRAGE Ontology and DCAT3 Extension Vocabulary for Glycomics Data Management

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OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models

A paper on using retrieval-augmented generation to improve biomedical code mapping leveraging ontology knowledge graphs and large language models.

The authors propose OntologyRAG, a method for better and faster biomedical code mapping. They leverage ontology knowledge graphs and large language models to improve the accuracy and efficiency of code mapping. The approach uses retrieval-augmented generation to combine the strengths of both knowledge graphs and language models.

Based on: OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models · Lecture notes in computer science

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CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning

A framework that enhances Large Language Models by retrieving relevant knowledge.

The CRP-RAG framework addresses limitations in existing Retrieval-Augmented Generation methods. It employs reasoning graphs to model complex query reasoning processes and guides knowledge retrieval, aggregation, and evaluation through these graphs. This approach outperforms baseline models in open-domain QA, multi-hop reasoning, and factual verification.

Based on: CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning · Electronics

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Enhancing Vector based Retrieval Augmented Generation with Contextual Knowledge Graph Construction

A novel approach to enhancing vector-based RAG models using contextual knowledge graph construction.

The authors introduce Contextual Knowledge Graph Construction (CKGC), a method that dynamically builds a knowledge graph to enhance information retrieval and question answering tasks. CKGC leverages text chunking, large language models, and ontology mapping to construct a contextualized knowledge graph. Experiments demonstrate significant improvements in Mean Reciprocal Rank and Top-k Accuracy.

Based on: Enhancing Vector based Retrieval Augmented Generation with Contextual Knowledge Graph Construction