The Hyperlexicon: A Multilingual, Sense-Centric Knowledge Graph with Hybrid Retrieval, Contextual Online Learning, and Business-Grade Interpretability

Introduction

The Hyperlexicon fuses symbolic and vector retrieval in a transparent, auditable framework. Built on a typed, weighted 
lexical-semantic graph with full-text and graph-based indices, it delivers provenance-grounded answers and
interactive subgraphs rather than black-box outputs. It is multilingual (with cross-lingual operations), leverages
standard open lexical resources, runs on lightweight storage/indexes, and learns online from user feedback.
Every result is explainable: contributing edges, named heuristics (e.g., domain/register alignment, recency), and 
re-ranking decisions are logged and traceable. Contextual online learning with smoothed updates adjusts weights on 
feedback; a lightweight re-ranker balances interpretability with low latency. Expansion follows Active inference 
(epistemic value vs. pragmatic performance).

What it is: A multilingual, sense-centric knowledge graph that blends full-text retrieval with a graph-based ANN vector
index and an interpretable reasoning layer.
Why it matters: Delivers transparent, provenance-grounded answers with business-grade latency
targets (p50 ≤ 30 ms; p95 ≤ 80 ms) on commodity infrastructure.
How it learns: Contextual online learning with smoothed updates; each user signal produces auditable ranking shifts.
Where it fits: Search, assistants, analytics, and compatible agent frameworks requiring explainability and low,
predictable latency.
Cost advantage: Estimated 0.1%–1% of comparable LLM-only approaches; sub-cent per user at
internet scale (see §6).
Risk controls: Tail-latency guardrails, named-policy logging, quarantine tags, and termination criteria for unbiased
stops.

Biomedical Use Case w/ BRCA1 Pathway and Node Schema

Interactive Mobile Data Summary