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Abstract. This is an investigation of information retrieval performance of Turkish search engines with respect to precision, normalized recall, coverage and novelty ratios. We defined seventeen query topics for Arabul, Arama, Netbul and Superonline. These queries were carefully selected to assess the capability of a search engine for handling broad or narrow topic subjects, exclusion of particular information, identifying and indexing Turkish characters, retrieval of hub/authoritative pages, stemming of Turkish words, correct interpretation of Boolean operators. We classified each document in a retrieval output as being "relevant" or "nonrelevant" to calculate precision and normalized recall ratios at various cut-off points for each pair of query topic and search engine. We found the coverage and novelty ratios for each search engine. We also tested how search engines handle meta-tags and dead links. Arama appears to be the best Turkish search engine in terms of average precision and normalized recall ratios, and the coverage of Turkish sites. Turkish characters (and stemming as well) still cause bottlenecks for Turkish search engines. Superonline and Netbul make use of the indexing information in metatag fields to improve retrieval results.

Meta-search, or the combination of the outputs of different search engines in response to a query, has been shown to improve performance. Since the scores produced by different search engines are not comparable, researchers have often decomposed the metasearch problem into a score normalization step followed by a combination step. Combination has been studied by many researchers. While appropriate normalization can affect performance, most of the normalization schemes suggested are ad hoc in nature.In this paper, we propose a formal approach to normalizing scores for meta-search by taking the distributions of the scores into account. Recently, it has been shown that for search engines the score distributions for a given query may be modeled using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. Here, it is shown that by equalizing the distributions of scores of the top non-relevant documents the best meta-search performance reported in the literature is obtained. Since relevance information is not available apriori, we discuss two different ways of obtaining a good approximation to the distribution of scores of non-relevant documents. One is obtained by looking at the distribution of scores of all documents. The second is obtained by fitting a mixture model of an exponential and a Gaussian to the scores of all documents and using the resulting exponential distribution as an estimate of the non-relevant distribution. We show with experiments on TREC-3, TREC-4 and TREC-9 data that the best combination results are obtained by averaging the parameters obtained from these approximations. These techniques work on a variety of different search engines including vector space search engines like SMART and probabilistic search engines like INQUERY.The problem of normalization is important in many other areas including information filtering, topic detection and tracking, multilingual search and distributed retrieval. Thus, the techniques proposed here are likely to be applicable to many of these tasks.

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.