Modelling a Query Space Using Associations

Mika Timonen, Paula Silvonen, Melissa Kasari

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

3 Citations (Scopus)

Abstract

We all use our associative memory constantly. Words and concepts form paths that we can follow to find new related concepts; for example, when we think about a car we may associate it with driving, roads or Japan, a country that produces cars. In this paper we present an approach for information modelling that is derived from human associative memory. The idea is to create a network of concepts where the links model the strength of the association between the concepts instead of, for example, semantics. The network, called association network, can be learned with an unsupervised network learning algorithm using concept co-occurrences, frequencies and concept distances. The possibility to create the network with unsupervised learning brings a great benefit when compared to semantic networks, where the ontology development usually requires a lot of manual labour. We present a case where the associations bring benefits over semantics due to easier implementation and the overall concept. The case focuses on a business intelligence search engine where we modelled its query space using association modelling. We utilised the model in information retrieval and system development.
Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXII
EditorsA. Heimbürger, Y. Kiyoki, T. Tokuda, H. Jaakkola, N. Yoshida
PublisherIOS Press
Pages77-96
ISBN (Electronic)978-1-60750-690-4
ISBN (Print)978-1-60750-689-8
DOIs
Publication statusPublished - 2011
MoE publication typeA3 Part of a book or another research book

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Volume225

Fingerprint

Semantics
Railroad cars
Data storage equipment
Competitive intelligence
Unsupervised learning
Search engines
Information retrieval
Learning algorithms
Ontology
Information systems
Personnel

Keywords

  • association network
  • association modelling
  • Human Associative Memory
  • query space modelling
  • information retrieval

Cite this

Timonen, M., Silvonen, P., & Kasari, M. (2011). Modelling a Query Space Using Associations. In A. Heimbürger, Y. Kiyoki, T. Tokuda, H. Jaakkola, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXII (pp. 77-96). IOS Press. Frontiers in Artificial Intelligence and Applications, Vol.. 225 https://doi.org/10.3233/978-1-60750-690-4-77
Timonen, Mika ; Silvonen, Paula ; Kasari, Melissa. / Modelling a Query Space Using Associations. Information Modelling and Knowledge Bases XXII. editor / A. Heimbürger ; Y. Kiyoki ; T. Tokuda ; H. Jaakkola ; N. Yoshida. IOS Press, 2011. pp. 77-96 (Frontiers in Artificial Intelligence and Applications, Vol. 225).
@inbook{5ea277e4042f49e9a07bf44155a05a1e,
title = "Modelling a Query Space Using Associations",
abstract = "We all use our associative memory constantly. Words and concepts form paths that we can follow to find new related concepts; for example, when we think about a car we may associate it with driving, roads or Japan, a country that produces cars. In this paper we present an approach for information modelling that is derived from human associative memory. The idea is to create a network of concepts where the links model the strength of the association between the concepts instead of, for example, semantics. The network, called association network, can be learned with an unsupervised network learning algorithm using concept co-occurrences, frequencies and concept distances. The possibility to create the network with unsupervised learning brings a great benefit when compared to semantic networks, where the ontology development usually requires a lot of manual labour. We present a case where the associations bring benefits over semantics due to easier implementation and the overall concept. The case focuses on a business intelligence search engine where we modelled its query space using association modelling. We utilised the model in information retrieval and system development.",
keywords = "association network, association modelling, Human Associative Memory, query space modelling, information retrieval",
author = "Mika Timonen and Paula Silvonen and Melissa Kasari",
note = "Project code: 35520",
year = "2011",
doi = "10.3233/978-1-60750-690-4-77",
language = "English",
isbn = "978-1-60750-689-8",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "77--96",
editor = "A. Heimb{\"u}rger and Y. Kiyoki and T. Tokuda and H. Jaakkola and N. Yoshida",
booktitle = "Information Modelling and Knowledge Bases XXII",
address = "Netherlands",

}

Timonen, M, Silvonen, P & Kasari, M 2011, Modelling a Query Space Using Associations. in A Heimbürger, Y Kiyoki, T Tokuda, H Jaakkola & N Yoshida (eds), Information Modelling and Knowledge Bases XXII. IOS Press, Frontiers in Artificial Intelligence and Applications, vol. 225, pp. 77-96. https://doi.org/10.3233/978-1-60750-690-4-77

Modelling a Query Space Using Associations. / Timonen, Mika; Silvonen, Paula; Kasari, Melissa.

Information Modelling and Knowledge Bases XXII. ed. / A. Heimbürger; Y. Kiyoki; T. Tokuda; H. Jaakkola; N. Yoshida. IOS Press, 2011. p. 77-96 (Frontiers in Artificial Intelligence and Applications, Vol. 225).

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

TY - CHAP

T1 - Modelling a Query Space Using Associations

AU - Timonen, Mika

AU - Silvonen, Paula

AU - Kasari, Melissa

N1 - Project code: 35520

PY - 2011

Y1 - 2011

N2 - We all use our associative memory constantly. Words and concepts form paths that we can follow to find new related concepts; for example, when we think about a car we may associate it with driving, roads or Japan, a country that produces cars. In this paper we present an approach for information modelling that is derived from human associative memory. The idea is to create a network of concepts where the links model the strength of the association between the concepts instead of, for example, semantics. The network, called association network, can be learned with an unsupervised network learning algorithm using concept co-occurrences, frequencies and concept distances. The possibility to create the network with unsupervised learning brings a great benefit when compared to semantic networks, where the ontology development usually requires a lot of manual labour. We present a case where the associations bring benefits over semantics due to easier implementation and the overall concept. The case focuses on a business intelligence search engine where we modelled its query space using association modelling. We utilised the model in information retrieval and system development.

AB - We all use our associative memory constantly. Words and concepts form paths that we can follow to find new related concepts; for example, when we think about a car we may associate it with driving, roads or Japan, a country that produces cars. In this paper we present an approach for information modelling that is derived from human associative memory. The idea is to create a network of concepts where the links model the strength of the association between the concepts instead of, for example, semantics. The network, called association network, can be learned with an unsupervised network learning algorithm using concept co-occurrences, frequencies and concept distances. The possibility to create the network with unsupervised learning brings a great benefit when compared to semantic networks, where the ontology development usually requires a lot of manual labour. We present a case where the associations bring benefits over semantics due to easier implementation and the overall concept. The case focuses on a business intelligence search engine where we modelled its query space using association modelling. We utilised the model in information retrieval and system development.

KW - association network

KW - association modelling

KW - Human Associative Memory

KW - query space modelling

KW - information retrieval

U2 - 10.3233/978-1-60750-690-4-77

DO - 10.3233/978-1-60750-690-4-77

M3 - Chapter or book article

SN - 978-1-60750-689-8

T3 - Frontiers in Artificial Intelligence and Applications

SP - 77

EP - 96

BT - Information Modelling and Knowledge Bases XXII

A2 - Heimbürger, A.

A2 - Kiyoki, Y.

A2 - Tokuda, T.

A2 - Jaakkola, H.

A2 - Yoshida, N.

PB - IOS Press

ER -

Timonen M, Silvonen P, Kasari M. Modelling a Query Space Using Associations. In Heimbürger A, Kiyoki Y, Tokuda T, Jaakkola H, Yoshida N, editors, Information Modelling and Knowledge Bases XXII. IOS Press. 2011. p. 77-96. (Frontiers in Artificial Intelligence and Applications, Vol. 225). https://doi.org/10.3233/978-1-60750-690-4-77