Finding software for reuse is a problem that programmers face. To reuse code that has been proven to work can increase any programmer's productivity, benefit corporate productivity, and also increase the stability of software programs. This paper shows that fuzzy retrieval has an improved retrieval performance over typical Boolean retrieval. Various methods of fuzzy information retrieval implementation and their use for software reuse will be examined. A deeper explanation of the fundamentals of designing a fuzzy information retrieval system for software reuse is presented. Future research options and necessary data storage systems are explored.

We present an integrated approach to information retrieval, which combines fuzzy clustering and fuzzy inference in order to improve textual retrieval performance. We capture the relationships among index terms by using fuzzy logic rules (with truth value assignment in [0,1]). We adapt fuzzy clustering methods (e.g., fuzzy c-means and fuzzy hierarchical clustering) in order to cluster documents with respect to the terms. The clusters generated provide a basis for building fuzzy logic rules concerning the terms, and the clusters can also be used to form hyperlinks between documents. The fuzzy logic rules are applied via fuzzy inference in order to derive query modification. In addition, relevance feedback is discussed as an alternative way to employ the fuzzy clusters. We explore retrieving an entire fuzzy cluster in response to a query. Finally, we note the need to test this approach more thoroughly on a larger standard test bed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We present an integrated approach to information retrieval which combines fuzzy clustering and fuzzy inference techniques in order to achieve optimal retrieval performance. To capture the relationships among index terms, fuzzy logic rules are used. We adapt several fuzzy clustering methods to the task of clustering documents with respect to the index terms. The clusters generated provide a basis for building the fuzzy logic rules. The clusters can also be used to form hyperlinks between documents. The fuzzy logic rules are applied with fuzzy inference to derive useful modifications of the initial query, which will guide the search for relevant documents. Alternative ways to use the fuzzy clusters are explored in this work as well. Our method combines fuzzy clustering and fuzzy inference with traditional relevance feedback approach for retrieval. The advantage of this approach is the emphasis on semantic information which relates the terms through the fuzzy clusters and fuzzy rules. A series of experiments have been conducted in order to validate this approach; a description of those experiments along with the results are presented.

Most document retrieval systems based on probabilistic models of feature distributions assume random selection of documents for retrieval. The assumptions of these models are met when documents are randomly selected from the database or when retrieving all available documents. A more suitable model for retrieval of a single document assumes that the best document available is to be retrieved first. Models of document retrieval systems assuming random selection and best-first selection are developed and compared under binary independence and two Poisson independence feature distribution models. Under the best-first model, feature discrimination varies with the number of documents in each relevance class in the database. A weight similar to the Inverse Document Frequency weight and consistent with the best-first model is suggested which does not depend on knowledge of the characteristics of relevant documents.

This paper outlines a problem in commercial online retrieval systems, provides a review of the relevant literature, and presents a solution for a special case of the problem. Previous investigators have considered how to best determine, for a ranked list of records retrieved from an online retrieval system, whether or not the user should continue to display the output. This paper examines the problem of how effective display size can be estimated as a means of assisting the users of commercial online retrieval systems. Although no experimental results are as yet available, the approach presented here will provide a guide to and prolegomenon for systematic study of the problem, as well as a method for providing the estimated number of relevant records remaining in a retrieved set ranked by a retrieval status value.

We shall consider retrieval performance measures for generalized (non-Boolean) queries and indexing functions. The meanings of recall and precision in such a generalized system will be discussed. Finally, we shall explore the meaning and difficulty of using such measures to compare Boolean and non-Boolean retrieval systems.

Abstract There has been a good deal of work on information retrieval systems that have continuous weights assigned to the index terms that describe the records in the database, and/or to the query terms that describe the user queries. Recent articles have analyzed retrieval systems with continuous weights of either type and/or with a Boolean structure for the queries. They have also suggested criteria which such systems ought to satisfy and record evaluation mechanisms which partially satisfy these criteria. We offer a more careful analysis, based on a generalization of the discrete weights. We also look at the weights from an entirely different approach involving thresholds, and we generate an improved evaluation mechanism which seems to fulfill a larger subset of the desired criteria than previous mechanisms. This new mechanism allows the user to attach a “threshold” to the query term.

Abstract The problems of resource allocation in the school library are analyzed and a practical operations research (O.R.) approach towards accountability is presented. A discussion of the nine‐step solution procedure is given, including the use of four planning instruments: inventory of services, preference form, data collection guide, and program costing matrix. The use of cost‐benefit analysis is shown to be helpful in determining the “best” allocation strategy. There is a presentation of implementation suggestions, and examples of the use of the methodology in actual school situations are given. Extensions of the work from building level school library media programs to district (system) and regional level learning resource (media) programs are also presented.

Abstract The retrieval decision problem is considered from the viewpoint of a decision theory approach. A threshold rule based on a suggested rule for indexing is considered and analyzed for retrieval decisions. The threshold rule is seen as a good descriptive measure of what a reasonable retrieval system should be able to accomplish. A retrieval mechanism of randomly drawing documents is analyzed to determine the relative strength of the threshold rule. The Neyman‐Pearson lemma is shown to yield a better a priori decision rule for retrieval; maximize precision subject to a fixed level of recall, instead of setting a lower limit upon precision, as does the threshold rule. The threshold rule is seen as a necessary, but not sufficient, condition for effective retrieval. A sufficient condition for the threshold rule illustrates the relationship between it and the rule derived from the Neyman‐Pearson lemma. Finally, a new measure of information retrieval system performance is presented, based on the threshold rule.

Abstract The Swets model of information retrieval, based on a decision theory approach, is discussed, with the overall performance measure being the crucial element reexamined in this paper. The Neyman‐Pearson criterion from statistical decision theory, and based on likelihood ratios, is used to determine an optimal range of Z, the variable assigned to each document by the retrieval system in an attempt to discriminate between relevant and nonrelevant documents. This criterion is shown to be directly related to both precision and recall, and is equivalent to the maximization of the expected value of the retrieval decision for a specific query and a given document under certain conditions. Thus, a compromise can be reached between those who advocate precision as a measure, due partially to its ability to be easily measurable empirically, and those who advocate consideration of recall. Several cases of the normal and Poisson distributions for the variable Z are discussed in terms of their implications for the Neyman‐Pearson decision rule. It is seen that when the variances are unequal, the Swets rule of retrieving a document if its Z value is large enough is not optimal. Finally, the situation of precision and recall not being inversely related is shown to be possible under certain conditions. Thus, this paper attempts to extend the understanding of the theoretical foundations of the decision theory approach to information retrieval.

article Free Access Share on Operations Research Applied to Document Indexing and Retrieval Decisions Authors: Abraham Bookstein The University of Chicago, Graduate Library School, 1100 East 57th St, Chicago, IL The University of Chicago, Graduate Library School, 1100 East 57th St, Chicago, ILView Profile , Don Kraft Department of Computer Science, Louisiana State University, Baton Rouge, LA Department of Computer Science, Louisiana State University, Baton Rouge, LAView Profile Authors Info & Claims Journal of the ACMVolume 24Issue 3July 1977 pp 418–427https://doi.org/10.1145/322017.322022Published:01 July 1977Publication History 37citation509DownloadsMetricsTotal Citations37Total Downloads509Last 12 Months14Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Abstract The problem of whether to bind, microcopy or discard back issues of journals in a university library branch system is considered and an algorithm is developed to solve the journal disposition problem. A cost‐effectiveness approach is pursued, employing an analytical model. This model uses a set of weighted factors to quantify the value of a specific journal to the library. Such factors as relevance, usage, availability elsewhere and capital investment are specified. Budget constraints, which involve the relevant costs of binding and microcopying, are considered, as are upper and lower threshold values on the worth of a journal to guarantee the retention of exceptionally good journals and the disposal of very poor ones. An example based on data from a real university special library situation is presented as an illustration of the model. Thus, this paper extends the work of others in modeling of the library collection development decision process.

Abstract A decision theory approach is used to model the information retrieval decision problem of which documents to retrieve from a library collection in response to a specific user query for information. A thorough discussion of decision theory, including the components of the alternatives, states‐of‐nature, outcomes, and evaluations–as well as of the optimization process under the cases of certainty, risk, and uncertainty–is presented. Bayesian statistics are also discussed to show how prior information about the various documents via classification analysis can affect the decision process under risk. An example problem is used to illustrate the decision theory approach and to compare the overall performance of the retrieval system under risk with and without the document classification information. Thus, the operations research technique of decision theory is used to model the retrieval decision process, illustrate how important evaluation is, and to demonstrate the value of prior information via document classification analysis. Moreover, the paper presents, in a somewhat tutorial mode, an overall framework for considering the information retrieval decision problem, incorporating the aspects of cost‐effectiveness and alternative evaluation, which allows one to better understand the contributions made by many researchers in this crucial area.