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 language | English |
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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) |
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Country | Finland |
City | Jyväskylä |
Period | 31/05/10 → 4/06/10 |
Keywords
- Association network
- Association modelling
- Human Associative Memory
- Query space modelling
- Information retrieval