paralinked

Paralinked is a concept in information architecture and data linking describing content items that are connected through parallel relationships rather than through a single, fixed hierarchy. In a paralinked model, each item may have multiple independent links to other items that reflect different aspects, contexts, or sources. The aim is to surface multiple facets of a relation and enable users to traverse related content along several tracks simultaneously.

Etymology and scope: the term is a portmanteau of parallel and linked, used in scholarly and industry discourse since the early 2010s, but it is not a standardized formalism. It serves as a descriptive label for a class of non-hierarchical, multi-relational link structures found in some knowledge graphs and metadata workflows.

Definition and characteristics: paralinked systems emphasize multiple, independent connections among entities, rather than a single parent-child pathway. They support cross-domain and cross-source linkages, potentially across languages, and maintain provenance information for each link. The emphasis is on surfacing alternative contexts and facets of related items.

Applications: paralinked concepts appear in knowledge graphs, digital archives, scientific literature databases, product catalogs, federated search interfaces, and editorial platforms. They are used to improve discovery, enable parallel navigation paths, and facilitate cross-context comparisons and data integration.

Implementation considerations: building paralinked structures relies on entity resolution, schema alignment, similarity scoring, and link ranking. It often involves graph databases, federated querying, and careful provenance governance to manage reliability and updates.

Example: in a scholarly knowledge graph, a single paper can be paralinked to its authors, cited works, datasets, translations, and related methods across multiple repositories, allowing users to explore related content through several parallel tracks.

See also: entity linking; knowledge graph; cross-linking; data integration.