HighlightCurated by Aramai Editorialgithub.com

Vault-LD: an open spec for Markdown vaults as linked data

An open format for knowledge that enables two-way conversion between Markdown notes and RDF graphs.

Vault-LD is a specification for converting Markdown notes into RDF graphs, enabling the sharing of knowledge between humans and machines. It uses YAML frontmatter to map onto YAML-LD, allowing for round-trip conversions between Markdown and RDF. This approach enables business semantics to be integrated into existing wiki systems, and vice versa.

Based on: GitHub - The-Knowledge-Graph-Guys/vault-ld: Vault-LD: an open spec for Markdown vaults as linked data. YAML-LD frontmatter + a shared @context = an RDF knowledge graph. Prose for humans and LLMs, triples for machines. · github.com

HighlightCurated by Aramai Editorialcloud.google.com

How the Open Knowledge Format can improve data sharing

Google introduces the Open Knowledge Format (OKF) to standardize knowledge representation for AI systems.

The Open Knowledge Format is an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. It represents knowledge as a directory of markdown files with YAML frontmatter and allows for standardized documentation and data sharing across teams and organizations. The OKF aims to solve the problem of fragmented context landscapes by providing a vendor-neutral, agent- and human-friendly standard for representing metadata, context, and curated knowledge.

Based on: How the Open Knowledge Format can improve data sharing | Google Cloud Blog · cloud.google.com

HighlightCurated by Aramai Editorialgithub.com

DuckDB RDF Extension

A DuckDB extension to read and write RDF files directly.

This extension allows reading and writing RDF files in DuckDB, supporting various formats such as Turtle, NTriples, NQuads, and TriG. It uses the SERD library for parsing and writing RDF data. The extension also supports WebAssembly (WASM) builds and compression formats like Gzip and Zst.

Based on: GitHub - nonodename/duck_rdf: RDF file extension for DuckDB. Reads and writes supported · github.com

HighlightCurated by Aramai Editorialgithub.com

GitHub - DataTreehouse/maplib

A high-performance RDF knowledge graph construction library in Python.

maplib is a Rust-based library for constructing and querying knowledge graphs. It supports SHACL validation, SPARQL and Datalog queries, and can read knowledge graphs from various serialization formats. The library allows users to leverage their existing skills with Pandas or Polars to extract and wrangle data before building a knowledge graph.

Based on: GitHub - DataTreehouse/maplib · github.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 EditorialarXiv

XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation

A framework that generates causally grounded explanations for GraphRAG systems.

The paper introduces XGRAG, a graph-native framework for explaining knowledge graph-based retrieval-augmented generation. It employs graph-based perturbation strategies to quantify the contribution of individual graph components on the model answer. The authors conduct experiments comparing XGRAG against an existing explainability baseline and evaluate its robustness across various question types and LLMs.

Based on: XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation · arXiv

HighlightCurated by Aramai EditorialarXiv

CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph

A framework for elevating web-scale corpus construction to structured knowledge organization.

The paper presents Cortex, a three-layer heterogeneous structure (Ontological Corpus Graph) for organizing high-quality corpora. It refines content, evolves ontologies, and enables cross-domain alignment. Comprehensive experiments validate its effectiveness in quality refinement, domain organization, and data synthesis.

Based on: CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph · arXiv

HighlightCurated by Aramai EditorialarXiv

SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering

A schema-aware cumulative process reward model for knowledge graph question answering.

The paper proposes SCPRM, a schema-aware cumulative process reward model for knowledge graph question answering. It aims to improve the performance of KGQA systems by incorporating schema information and cumulative rewards. The authors evaluate SCPRM on several benchmark datasets and demonstrate its effectiveness compared to existing methods.

Based on: SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering · arXiv

HighlightCurated by Aramai EditorialarXiv

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

HighlightCurated by Aramai Editorialopenalex.org

Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization

A generative AI system integrating Retrieval-Augmented Generation, Vector Stores, and Knowledge Graphs for legal information retrieval.

The paper presents a jurisdiction-specific legal information retrieval system that combines Retrieval-Augmented Generation, Vector Stores, and Knowledge Graphs constructed via Hierarchical Non-Negative Matrix Factorization. The system is designed to enhance information retrieval and AI reasoning in the legal domain, minimizing hallucinations. It empowers AI agents to identify complex connections among cases, statutes, and legal precedents.

Based on: Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization

HighlightCurated by Aramai EditorialJournal of Industrial Information Integration

Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis

This paper proposes integrating knowledge graphs within a retrieval-augmented generation framework for failure mode and effects analysis data.

The authors propose enhancing retrieval-augmented generation with a knowledge graph to leverage analytical and semantic question-answering capabilities for FMEA data. They present set-theoretic standardization, an algorithm for creating vector embeddings from the FMEA-KG, and a KG-enhanced RAG framework. The approach is validated through a user experience design study.

Based on: Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis · Journal of Industrial Information Integration

HighlightCurated by Aramai Editorialopenalex.org

How to Build an Adaptive AI Tutor for Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)

A paper introducing a novel framework for developing adaptable AI tutoring systems using knowledge graphs and retrieval-augmented generation.

The paper presents a framework called KG-RAG, which integrates structured knowledge representation with context-aware retrieval to improve AI tutoring. It addresses challenges in maintaining factual accuracy and delivering coherent instruction. The authors provide empirical validation through controlled experiments demonstrating significant learning improvements.

Based on: How to Build an Adaptive AI Tutor for Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)