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• W2188012532 abstract "It is indeed the situation that the fields of Case-Based Reasoning (CBR) and Information Retrieval (IR) a number of shared interests. This paper addresses one in particular: indexing or the representation of information. It is very important to be able to describe each case or text effectively. Items which are not appropriately indexed are in effect lost in memory and are unlikely to be retrieved under appropriate circumstances. Therefore indexing is a critical issue and is the focus here. In particular this paper proposes a structured approach, similar to case indexing, for the representation of text content. It also proposes similarity based retrieval strategies, in the context of the proposed structured representation. These ideas have been implemented in Empiricist, a prototype retrieval system. Case-based reasoning is the essence of how human reasoning works [12]. People reason from experience. A case-based reasoner finds those cases in memory that solved problems similar to the current problem. In order to find a relevant case we need to characterize the input problem, by assigning appropriate features to it. Then we retrieve cases from memory with features that best match the input problem. Solutions used for retrieved cases are adapted to fit the current problem [12]. Case retrieval is facilitated by characterizing each case (and problem) by a variety of structural features such as the events, goals, and actors involved in the case [7]. Information Retrieval is similar in that texts and user requests are also represented by their features. Retrieval also involves a comparison of features to identify texts that best match the request. However, there are significant differences in the types of representations used in both fields. Representations in CBR tend to be richer and more informative because they use structured descriptions. For example, the relationship between an actor and an event is generally preserved. In IR, text representation is typically done by assigning a groups of keywords to a text. In fact, the lack of structure in keyword representation limits the versatility and effectiveness of IR systems. In this paper we present Empiricist, a prototype retrieval system in which text representation deviates from the norm. In particular, we show that IR can utilize structured representations similar to the ones used in CBR. Our texts are represented by structured complex objects. The complex object provides a rich framework for representing text content and also supports sophisticated retrieval strategies. In essence this paper makes a specific contribution to the question posed in the symposium’s call for papers: How might IR take advantage of more structured information, as used in CBR? We also discuss retrieval in the context of these complex object text representations." @default.
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• W2188012532 title "Structured Representations of Empirical Information" @default.
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