SemMemDB

In-database knowledge activation

Yang Chen, Milenko Petrovic, Micah Clark

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

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)
Pages18-23
Number of pages6
StatePublished - Jan 1 2014
Event27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014 - Pensacola, United States
Duration: May 21 2014May 23 2014

Other

Other27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014
CountryUnited States
CityPensacola
Period5/21/145/23/14

Fingerprint

Chemical activation
Data storage equipment
Semantics
Query languages
Ontology
Engines

All Science Journal Classification (ASJC) codes

  • Computer Science Applications

Cite this

Chen, Y., Petrovic, M., & Clark, M. (2014). SemMemDB: In-database knowledge activation. 18-23. Paper presented at 27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014, Pensacola, United States.
Chen, Yang ; Petrovic, Milenko ; Clark, Micah. / SemMemDB : In-database knowledge activation. Paper presented at 27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014, Pensacola, United States.6 p.
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Chen, Y, Petrovic, M & Clark, M 2014, 'SemMemDB: In-database knowledge activation' Paper presented at 27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014, Pensacola, United States, 5/21/14 - 5/23/14, pp. 18-23.

SemMemDB : In-database knowledge activation. / Chen, Yang; Petrovic, Milenko; Clark, Micah.

2014. 18-23 Paper presented at 27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014, Pensacola, United States.

Research output: Contribution to conferencePaper

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AU - Clark, Micah

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N2 - 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.

AB - 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.

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Chen Y, Petrovic M, Clark M. SemMemDB: In-database knowledge activation. 2014. Paper presented at 27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014, Pensacola, United States.