SemMemDB: In-database knowledge activation

Yang Chen, Milenko Petrovic, Micah H. Clark

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations


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 web-scale ontology constructed from the Wikipedia corpus. The results show that our system runs over 500 times faster than previous works.

Original languageEnglish (US)
Number of pages6
StatePublished - 2014
Event27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014 - Pensacola, United States
Duration: May 21 2014May 23 2014


Other27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Science Applications


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