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It ingests fragmented enterprise data, extracts entities and relations, builds a Context Graph and knowledge graph, and runs graph analytics and causal reasoning on top. Graph construction, reasoning, and decision provenance are deterministic and do not require an LLM; when an LLM is needed, every major provider can be swapped in. It targets regulated domains such as finance and healthcare that demand explainable and auditable AI.\u003C\u002Fp>\n\u003Cblockquote>\n\u003Cp>Replace plain vector similarity with a structured semantic graph, so every AI conclusion can be traced to its source.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Value\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Platform\u003C\u002Ftd>\n\u003Ctd>Python 3.10+\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>License\u003C\u002Ftd>\n\u003Ctd>MIT\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Graph storage\u003C\u002Ftd>\n\u003Ctd>Multiple backends (RDF and property graphs)\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Standards\u003C\u002Ftd>\n\u003Ctd>W3C (OWL, SHACL, SKOS)\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"key-features\">Key Features\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Data ingestion\u003C\u002Fstrong>: connect multi-source enterprise data and extract entities, relations, and attributes\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Context Graph\u003C\u002Fstrong>: organize business context into a structured, queryable semantic graph\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Deterministic reasoning\u003C\u002Fstrong>: graph construction and inference do not depend on an LLM, so results are reproducible\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Ontology management\u003C\u002Fstrong>: define entity meanings and rules with OWL, SHACL, and SKOS standards\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Decision provenance\u003C\u002Fstrong>: every conclusion carries a full audit chain to answer why\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Polyglot storage\u003C\u002Fstrong>: supports both RDF and property graphs across multiple graph databases\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Optional LLM\u003C\u002Fstrong>: plug in OpenAI, Anthropic, Gemini, and more via \u003Ccode>semantica.llms\u003C\u002Fcode>, vendor neutral\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"target-audience\">Target Audience\u003C\u002Fh2>\u003Cul>\n\u003Cli>Data engineers building enterprise knowledge graphs\u003C\u002Fli>\n\u003Cli>Agent teams that need explainable reasoning\u003C\u002Fli>\n\u003Cli>AI leads in regulated industries such as finance and healthcare\u003C\u002Fli>\n\u003Cli>Developers building GraphRAG retrieval systems\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"quick-start\">Quick Start\u003C\u002Fh2>\u003Col>\n\u003Cli>Install:\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">pip install semantica\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>Configure data sources and ontologies per the official docs to start ingesting and building graphs\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"core-advantages\">Core Advantages\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Explainable\u003C\u002Fstrong>: structured graphs and provenance chains make conclusions auditable\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Deterministic\u003C\u002Fstrong>: inference does not rely on probabilistic models; same input, same output\u003C\u002Fli>\n\u003Cli>\u003Cstrong>No vendor lock-in\u003C\u002Fstrong>: open source, optional LLMs, and open standards\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"system-requirements\">System Requirements\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Requirement\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>OS\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Runtime\u003C\u002Ftd>\n\u003Ctd>Python 3.10+\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"notes\">Notes\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Scope of explanation\u003C\u002Fstrong>: it explains system-level reasoning chains and does not expose what happens inside an LLM\u003C\u002Fli>\n\u003Cli>\u003Cstrong>LLM optional\u003C\u002Fstrong>: graph construction and provenance never require an LLM; connect one only when needed\u003C\u002Fli>\n\u003C\u002Ful>\n",1793248613796]