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Spreading activation search

The fact keywords can be associated with documents, that keywords can be associated with one another, documents become associated by citations, etc. lead early IR pioneers such as Doyle and Stiles to adopt simple association as a unifying relation connecting many objects of the FOA inquiry [REF317] [Doyle61] [Doyle62] [REF322] [Maron82] [Giuliano63] [Findler79] . Integrating information across SEMANTIC NETWORKS of such associative relations has been an important example of knowledge representation within AI since the memory models of Collins and Qullian [REF170] . In its simplest form, a simple quantity known as ACTIVITY is allowed to propagate through a network like that shown in Figure (figure) from several sources. SPREADING ACTIVATION SEARCH is the name for a broad range techniques that find solutions (for example, a path from Start to Goal in Figure (FOAref) ) by controlling the propagation of activity through associative networks like this [REF321] [REF668] [REF667] .

The Adaptive Information Retrieval (AIR) system was a prototype search engine built as part of my dissertation at the University of Michigan in the mid-1980s [REF222] [REF622] . This research was part one of several systems applying CONNECTIONIST (neural network) learning methods to the IR search engine problem [REF568] [REF732] [REF773] [REF430] .

Figure (figure) shows how many of the features discussed here can interact as part of a single retrieval system. This figure comes from Dan Rose's SCALIR (Symbolic and Connectionist Approach to Legal Information Retrieval) system, built to investigate the use of both logical, ``symbolic'' modes of inference and probabalitic, ``subsymbolic'' ones. This figure shows containment relations between document elements, (like those shown in more detail in (FOAref) ) topical connections between keywords, and inter-document citations, all mixed and used as part of spreading activation-based inference.


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