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wware / BFS-QL-MCP.md
Last active March 22, 2026 14:06

Linked Data as MCP — Product Description

What It Is

A SaaS service that turns any linked data resource into an MCP endpoint your chatbot can query.

You give it a URL or connection string. It gives you back an MCP link. You paste that link into your MCP-capable chat client (Claude, Cursor, etc.). Your chatbot can now traverse and reason over that resource intelligently — without you or it

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wware / IdentityServer.md
Created March 17, 2026 18:46

Identity Server Specification

The identity server is the authoritative component for entity identity across the knowledge graph. It handles the full lifecycle of an entity's identity: resolving a mention to an ID, promoting a provisional entity to canonical status, detecting synonyms, and merging duplicates into a single survivor. All of this must be correct under concurrent access from multiple worker processes and multiple server replicas.

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wware / Ingestion.md
Last active March 17, 2026 18:38

Ingestion Architecture

This document describes the ingestion pipeline for the kgraph/medlit system.

Design Principles

Dedup-on-write. The IdentityServer resolves entity identity and detects synonyms incrementally as each entity is written. There is no global deduplication

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wware / Future_Directions.md
Last active February 10, 2026 06:33
Future directions for the knowledge graph stuff
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wware / Plan9.md
Last active February 3, 2026 20:58
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wware / Whole_and_complete_1.md
Last active January 23, 2026 20:40
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wware / Bigger_Picture.md
Last active January 19, 2026 16:55

Generalizing literature graphs across knowledge domains

Prompt:

1/18/2026, 10:16:42 AM

I want to reframe the medical literature project a bit, allow it to be generalized to other domains of knowledge. We are still building a graph and a graph still consists of nodes (entities) and edges (relationships). We still have a collection of entities from previous ingestion processes. We add a new thing: entities may be "canonical", that is they have been assigned canonical IDs (UMLS numbers or whatever) or they may be "provisional", meaning that we don't know yet if they should be assigned canonical IDs, for instance an entity might be a mention of some trivial thing in just one paper.

Given a batch of papers to ingest, we proceed in two passes. First pass we extract entities and assign canonical IDs where they make sense. Second pass we identify the edges (for medical, these edges are of the three types, extraction, claims, and evidence). The first pass produces a JSON serialization of the collection of enti

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wware / Battle_plan.md
Last active January 18, 2026 04:08

Battle Plan: Medical Knowledge Graph Ingestion Optimization

This document outlines the strategic roadmap for integrating Large Language Models (LLMs) into the ingestion pipeline, specifically targeting Stage 4 (Claims Extraction) and Stage 5 (Evidence Aggregation).

1. LLM Infrastructure Strategy

The choice of model is critical for handling the specialized technical language and structured data extraction requirements of medical research papers.

Component Model Recommendation Rationale
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wware / 1_test_suite.md
Last active January 13, 2026 23:09

Short answer: yes, this test suite makes a lot of sense, and it’s actually quite strong architecturally. Longer answer below, with praise and some concrete suggestions where you might tighten it.

What you’re doing well

1. Clear layering and intent

Your tests map very cleanly onto your system architecture:

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wware / SBIR.md
Last active January 11, 2026 14:34