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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 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

An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration

A paper proposing an agent-oriented pluggable retrieval orchestration layer for experience-driven retrieval strategy selection.

The authors present Experience-RAG Skill, a layer positioned between the agent and retriever pool. It analyzes the scene, consults an experience memory, selects a retrieval strategy, and returns structured evidence to the agent. The proposed skill outperforms fixed single-retriever baselines on various tasks.

Based on: An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration · arXiv

HighlightCurated by Aramai EditorialarXiv

Uncertainty-Aware Hybrid Retrieval for Long-Document RAG

A training-free hybrid retrieval framework for Retrieval Augmented Generation (RAG).

The authors propose Uncertainty-aware Multi-Granularity RAG (UMG-RAG), a hybrid retrieval framework that treats chunk granularity as query-specific reliability estimation. UMG-RAG uses existing dense and sparse retrievers as complementary experts across multiple chunk granularities, estimating reliability from distribution entropy and fusing candidates according to query-specific semantic, lexical, and granularity confidence.

Based on: Uncertainty-Aware Hybrid Retrieval for Long-Document RAG · 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 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

SeedER: Seed-and-Expand Retrieval from Knowledge Graphs

A retrieval framework for knowledge graphs that leverages structure through iterative expansion.

The authors introduce SeedER, a retrieval framework for knowledge graphs that explicitly uses graph structure to improve efficiency and effectiveness.,SeedER first seeds a compact set of core nodes using lightweight dense and entity-based retrieval, then selectively expands this set via a learned policy.,This design enables efficient discovery of query-relevant nodes while controlling expansion cost.

Based on: SeedER: Seed-and-Expand Retrieval from Knowledge Graphs · arXiv

HighlightCurated by Aramai EditorialarXiv

DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation

A framework for learnable evidence control in multi-hop retrieval-augmented generation.

The paper introduces DynaKRAG, a unified framework that formulates multi-hop evidence acquisition as state-conditioned control over atomic evidence operations. It uses a learned controller to select the next operation and updates the evidence state accordingly. The authors evaluate DynaKRAG on several benchmarks and demonstrate its effectiveness in coordinating retrieval, diagnosis, and gap-directed acquisition.

Based on: DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation · arXiv