Aseems

Aseems (Adaptive Semantic Extraction and Enrichment Management System) is a term used to describe a conceptual framework for turning unstructured data into structured knowledge. In many discussions, Aseems denotes a configurable pipeline that combines natural language processing, semantic enrichment, and graph-based storage to support knowledge graphs and linked-data applications. For the purposes of this article, Aseems is presented as a hypothetical reference architecture rather than a single sold product.

Its core idea is to provide end-to-end processing: ingest data from multiple sources, extract entities and relations, disambiguate and link findings to external ontologies, and store the enriched information in a graph database. Typical components include an ingestion layer, NLP and information-extraction modules, a cross-document disambiguation and linking engine, an ontology alignment and enrichment module, a reasoning or inference component, and a flexible API layer for governance and querying.

Common use cases described in literature include building knowledge graphs for enterprises, semantic search and discovery, metadata management, and content recommendation systems. Because Aseems is a broad blueprint rather than a specific product, real-world implementations vary in scope, performance, and tooling, with many projects adapting the architecture to domain needs and available technologies.

See also: Semantic web, Knowledge graph, Natural language processing, Data integration.