Views: a hardware-aware recursively labeled graph database model for knowledge representation and reasoning

Knowledge representation remains a central challenge for reasoning-centered artificial intelligence, particularly when semantic structures involve relations over relations, contextual annotations, and recursively nested descriptions. This paper introduces Views, a recursively labeled graph database (GDB) model designed to represent such graph-structured knowledge within a uniform graph abstraction while retaining a hardware-aware organization for associative search and traversal.

The model refactors directed labeled graphs into linked-list-like chains of linknodes, supports recursive labeling of vertices and edges, and admits mappings from Resource Description Framework (RDF)- and labeled property graph (LPG)-style graph representations. We describe the data structure, its hardware-oriented memory mappings under the Associative Chip Architecture (ASOCA), and selected associative operations for retrieval over Views-based GDBs. We then evaluate storage footprint under stated mapping and store boundaries using three scales of the Social Network Benchmark published by the Linked Data Benchmark Council, and characterize directed K-hop traversal on an Associative Memory Chip III (ASOCA3) field-programmable gate array (FPGA) implementation.

The storage results show that allocation and entry width materially affect the reported footprints rather than establishing an intrinsic advantage for Views, while the K-hop measurements show increasing mean returned-vertex count and mean latency with hop bound over the fixed resident graph image. Worked semantic-reasoning and Copycat-inspired examples further illustrate how retrieval operations can be composed over the model, rather than providing application-level reasoning or cognitive validation.

Taken together, these results position Views as a model-architecture co-design for associative storage and selected graph traversal under the stated conditions; broader database workloads and end-to-end reasoning applications remain to be evaluated.