Abstract
| Original language | English |
|---|---|
| Title of host publication | Information Modelling and Knowledge Bases XXII |
| Subtitle of host publication | 20th European-Japanese Conference on Information Modelling and Knowledge Bases, EJC 2010 |
| Publication status | Published - 2010 |
| MoE publication type | A4 Article in a conference publication |
| Event | Information Modelling and Knowledge Bases XXII, 20th European-Japanese Conference on Information Modelling and Knowledge Bases (EJC 2010) - Jyväskylä, Finland Duration: 31 May 2010 → 4 Jun 2010 |
Conference
| Conference | Information Modelling and Knowledge Bases XXII, 20th European-Japanese Conference on Information Modelling and Knowledge Bases (EJC 2010) |
|---|---|
| Country | Finland |
| City | Jyväskylä |
| Period | 31/05/10 → 4/06/10 |
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Keywords
- Association network
- Association modelling
- Human Associative Memory
- Query space modelling
- Information retrieval
Cite this
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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 proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Modelling a query space using associations
AU - Timonen, Mika
AU - Silvonen, Paula
AU - Kasari, Melissa
N1 - Project code: 35520
PY - 2010
Y1 - 2010
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
M3 - Conference article in proceedings
SN - 978-1-60750-689-8
BT - Information Modelling and Knowledge Bases XXII
ER -