Knowledge Engineering

A dual architecture — semantic memory and a knowledge graph — serving fidelity before intelligence, behind an open-standard MCP connector that links the corpus to any AI assistant.

🧠 Semantic memory

Passages embedded with fourth-generation multilingual embeddings alongside a custom Arabic lexical index. Retrieval is hybrid: meaning and term fused by reciprocal rank, then reranked — capturing both the modern question and the classical term.

🕸️ Knowledge graph

Concepts, scholars and relations (grounds, critiques, explains, branches from…) are extracted from passages, and every relation carries a verbatim supporting quote with its page — the whole graph is human-auditable, thread by thread.

⬜ Principle: empty is valid

The extractor is forbidden to infer beyond the text; a passage with no explicit relation remains relation-free. Precision over completeness.

The research connector

The corpus is served over the Model Context Protocol with OAuth 2.1 sign-in and a governed access list. All tools are read-only: hybrid search, graph exploration, thematic communities, book card, sequential reading, and service description.

For the technically curious

Ingestion stages — semantic memory
Knowledge graph & archival

Memory answers: what was said about X? The graph answers: how does X relate to Y? Together they cover the direct question, the relational question, and the map of a subject.