Semantic networks are a popular way of simulating human memory in ACT-R-like cognitive architectures. However, existing implementations fall short in their ability to efficiently work with very large networks required for full-scale simulations of human memories. In this paper, we present SemMemDB, an in-database realization of semantic networks and spreading activation. We describe a relational representation for semantic networks and an efficient SQL-based spreading activation algorithm. We provide a simple interface for users to invoke retrieval queries. The key benefits of our approach are: (1) Databases have mature query engines and optimizers that generate efficient query plans for memory activation and retrieval; (2) Databases can provide massive storage capacity to potentially support human-scale memories; (3) Spreading activation is implemented in SQL, a widely-used query language for big data analytics. We evaluate SemMemDB in a comprehensive experimental study using DBPedia, a webscale ontology constructed from the Wikipedia corpus. The results show that our system runs over 500 times faster than previous works.
Authors:
Yang Chen, Milenko Petrovic, Micah H. Clark
Bibtex:
@article{,
author = "Yang Chen, Milenko Petrovic, Micah H. Clark",
title = "SemMemDB: In-Database Knowledge Activation",
journal = "Proceedings of the 27th International FLAIRS Conference",
year = "2014"
}
Download:
[pdf]