Modelling a query space using associations

Mika Timonen, Paula Silvonen, Melissa Kasari

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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
Subtitle of host publication20th European-Japanese Conference on Information Modelling and Knowledge Bases, EJC 2010
Publication statusPublished - 2010
MoE publication typeA4 Article in a conference publication
EventInformation Modelling and Knowledge Bases XXII, 20th European-Japanese Conference on Information Modelling and Knowledge Bases (EJC 2010) - Jyväskylä, Finland
Duration: 31 May 20104 Jun 2010

Conference

ConferenceInformation Modelling and Knowledge Bases XXII, 20th European-Japanese Conference on Information Modelling and Knowledge Bases (EJC 2010)
CountryFinland
CityJyväskylä
Period31/05/104/06/10

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. (2010). Modelling a query space using associations. In Information Modelling and Knowledge Bases XXII: 20th European-Japanese Conference on Information Modelling and Knowledge Bases, EJC 2010
Timonen, Mika ; Silvonen, Paula ; Kasari, Melissa. / Modelling a query space using associations. Information Modelling and Knowledge Bases XXII: 20th European-Japanese Conference on Information Modelling and Knowledge Bases, EJC 2010. 2010.
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Timonen, M, Silvonen, P & Kasari, M 2010, Modelling a query space using associations. in Information Modelling and Knowledge Bases XXII: 20th European-Japanese Conference on Information Modelling and Knowledge Bases, EJC 2010. Information Modelling and Knowledge Bases XXII, 20th European-Japanese Conference on Information Modelling and Knowledge Bases (EJC 2010), Jyväskylä, Finland, 31/05/10.

Modelling a query space using associations. / Timonen, Mika; Silvonen, Paula; Kasari, Melissa.

Information Modelling and Knowledge Bases XXII: 20th European-Japanese Conference on Information Modelling and Knowledge Bases, EJC 2010. 2010.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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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.

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Timonen M, Silvonen P, Kasari M. Modelling a query space using associations. In Information Modelling and Knowledge Bases XXII: 20th European-Japanese Conference on Information Modelling and Knowledge Bases, EJC 2010. 2010