F. Crestani. Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11(6):453–482, December 1997. [pdf]
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This paper contains a literature review on the theme of spreading activation techniques used in information retrieval, which is a prticular instance of associative retrieval. The paper offers, at first, an overview of previeus research on this theme focussing on the associative retrieval with spreading activation.
One of the most common technique to build the associative network used by this technique is to expanding the original query with term-term, term-document and document-document associations. This technique is based on the assumption that there are statistically determinable relations among the tems, and between the terms and the documents. This assumption is also accounted for some of the drawback of the methods, because it is questioned the general applicability of the similarity measures derived statistically in domains which are extern to the knowledge domain. Additionally these similarities are computed under the assumption that the terms in the documents are originally uncorrelated.
The rest of the paper shows a couple of applications using Spreading Activation for IR. Some of the result shows a lack of consisten improvement of the effectivenes of the retrieval over other methods (i.e., vector processing model). Additionally, one of the drawback of SA is the difficulty of building and maintaining the network of connections between the elements (i.e., documents and terms).
One interesting application is that of Kimoto and Iwadera, AIRS, which propose to use an SA technique in connection with a dynamic thesaurus, which is a network where nodes represent terms and links represent semantic relationship between terms.
Another interesting idea proposed in the paper is that of using automatic constructed hypertext to facilitate the IR with the SA technique.
Tags: information retrieval, Latent Semantic Analysis, machine learning, Singular Value Decomposition, text data mining