The Cortical Activity of Graded Relevance

This chapter aims to master web mining and Information Retrieval (IR) in the digital age, thus describing the overviews of web mining and web usage mining; the significance of web mining in the digital age; the overview of IR; the concept of Collaborative Information Retrieval (CIR); the evaluation of IR systems; and the significance of IR in the digital age. Web mining can contribute to the increase in profits by selling more products and by minimizing costs. Web mining is the application of data mining techniques to discover the interesting patterns from web data in order to better serve the needs of web-based multifaceted applications. Mining web data can improve the personalization, create the selling opportunities, and lead to more profitable relationships with customers in global business. Web mining techniques can be applied with the effective analysis of the clearly understood business needs and requirements. Web mining builds the detailed customer profiles based on the transactional data. Web mining is used to create the personalized search engines which can recognize the individuals' search queries by analyzing and profiling the web user's search behavior. IR is the process of obtaining relevant information from a collection of informational resources. IR has considerably changed with the expansion of the Internet and the advent of modern and inexpensive graphical user interfaces and mass storage devices. The effective IR system, including an active indexing system, not only decreases the chances that information will be misfiled but also expedites the retrieval of information. Regarding IR utilization, the resulting time-saving benefit increases office efficiency and productivity while decreasing stress and anxiety. Most IR systems provide the advanced searching capabilities that allow users to create the sophisticated queries. The chapter argues that applying web mining and IR has the potential to enhance organizational performance and reach strategic goals in the digital age.

The primary objective of this study was to further characterize the role of sex, if any, in event-related potentials (ERPs). More specifically, we aimed to investigate sex sensitivity with respect to the P300 ERP. Each female and male study participant underwent an oddball paradigm electroencephalography ERP session. ERP data were subjected to preprocessing and postprocessing, as well as statistical analysis. The results of the study showed that men had larger P300 amplitudes on average for both low-probability and high-probability stimuli compared with women (P<0.001). Men also showed shorter P300 latencies on average than did women (P<0.001). Significant differences in ERPs between men and women were observed in this study. The results indicate the existence of sex sensitivity to the P300 ERP, which may be because of men eliciting higher response inhibition compared with women.

Purpose – People often engage in different information-seeking strategies (ISSs) within a single information-seeking episode. A critical concern for the design of information retrieval (IR) systems is how to provide support for these different behaviors in a manner which searchers can easily understand, navigate and use, as they move from one ISS to another. The purpose of this paper is to describe a dialogue structure that was implemented in an experimental IR system, in order to address this concern. Design/methodology/approach – The authors conducted a user-centered experiment to evaluate the IR systems. Participants were asked to search for information on two different task types, with four different topics per task, in both the experimental system and a baseline system emulating state-of-the-art IR systems. The authors report here the results related explicitly to the use of the experimental system's dialogue structure. Findings – For one of the task types, most participants followed the search steps as predicted in the dialogue structures, and those who did so completed the task in fewer moves. For the other task type, predicted order of moves was often not followed, but participants again used fewer moves when following the predicted order. Results demonstrate that the dialogue structures the authors designed indeed support effective human information behavior patterns in a variety of ways, and that searchers can effectively use a system which changes to support different ISSs. Originality/value – This study shows that it is both possible and beneficial, to design an IR system which can support multiple ISSs, and that such a system can be understood and used successfully.

Information retrieval (IR) systems aim to select relevant material from large collections of information in response to user queries. The approaches used to accomplish this have been the focus of much research and development over the past 50 years, and have led to the algorithms underlying many commercial and Web-based search engines today. This entry describes the common components that go into the design of IR systems (from text processing to inverted file indexes). The major classes (or models) of retrieval algorithms (Boolean, vector, and probabilistic) are described along with formal definitions of the basic form of these algorithms and some of the variations in common use in IR research. In addition, the entry examines query expansion techniques, and in particular relevance feedback, and how they are used in IR systems

Evaluation of information retrieval systems is a fundamental topic in Library and Information Science. The aim of this paper is to connect the system-oriented and the user-oriented approaches to relevant philosophical schools. By reviewing the related literature, it was found that the evaluation of information retrieval systems is successful if it benefits from both system-oriented and user-oriented approaches (composite). The system-oriented approach is rooted in Parmenides’ philosophy of stability (immovable) which Plato accepts and attributes to the world of forms; the user-oriented approach is rooted in Heraclitus’ flux philosophy (motion) which Plato defers and attributes to the tangible world. Thus, using Plato’s theory is a comprehensive approach for recognizing the concept of relevance. The theoretical and philosophical foundations determine the type of research methods and techniques. Therefore, Plato’s dialectical method is an appropriate composite method for evaluating information retrieval systems.

This paper reviews the concept and goal of Information Retrieval Systems (IRSs). It also explains the synonymous concepts in Information Retrieval (IR)which include such terms as: imprecision, vagueness, uncertainty, and inconsistency. Current trends in IRSs are discussed. Fuzzy Set Theory, Fuzzy Retrieval Modelsare reviewed. The paper also discusses extensions of Fuzzy Boolean Retrieval Models including Fuzzy techniques for documents’ indexingandFlexible query languages. Fuzzy associative mechanisms were identified to include:(1)fuzzy pseudothesauri and fuzzy ontologies which can be used to contextualize the search by expanding the set of index terms of documents;(2)an alternative use of fuzzy pseudothesarui and fuzzy ontologies is to expand the query with related terms by taking into account their varying importance of an additional termand (3)fuzzy clustering techniques, where each document can be placed within several clusters with a given strength of belonging to each cluster, can be used to expand the set of the documents retrieved in response to a query.The paper concludesby recommending that in an electronic library environment, the librarians and information scientists should acquaint themselves with these terms in order to be more equipped in helping library users retrieve online documents relevant to their information needs.

Past research has identified many different types of relevance in information retrieval (IR). So far, however, most evaluation of IR systems has been through batch experiments conducted with test c...

Stemming is a basic morphological analysis tool or a process used to remove variants of a particular word and to identify its root or stem. In this, morphological analysis is carried out to identify various forms of a word to obtain its stem. Stemming is used as a preprocessing operational tool for the development of various natural language text applications, such as part-of-speech tagging, sentiment analysis, text segmentation, text classification, text summarization, information extraction, information retrieval applications, and named entity recognition. Stemming is generally used to achieve better performance and evaluation of information retrieval systems. It is achieved by increasing the standard in the format of the text. There may be various forms of the same root word or base that are used in the system database to increase storage requirements by stemming. Index files are reduced to the words present in the text with their linguistic variants in the documents. It is useful for memory management and system performance and for reducing memory requirements. Our proposed system is designed specifically for Marathi language text because for some other languages in Deonagri scripts like Hindi or Konkani stemmers are available and have good results and performance. But for Marathi, very little work has been done on stemmers. The proposed idea is to develop a stemmer for the Marathi language by using a supervised machine learning approach and our own developed handwritten grammar rule set and word dictionary using which the suffixes of the different variants are successfully removed for generating their respective stem words. Finally, the system is evaluated for its performance. It is measured at 61.34%.KeywordsNLPStemmingLinguistic variantsSuffix removalDeonagri scripts

Although significant efforts have been devoted to the study and evaluation of information retrieval systems from an algorithmic perspective, far less work has been performed on the evaluation of these systems from the user's perspective. This is certainly the case for Web information retrieval, where the major search engines continue to utilise interfaces that have not changed substantially since their introduction. One of the challenges in developing new Web search interfaces is the evaluation of these systems in comparison to one another, as well as in comparison to the popular Web search engines. In this paper, we highlight some of the methods used in the literature for evaluating Web search systems, and present a summary of the methods that we have found to be effective in dealing with the challenges of evaluating intelligent and interactive Web search interfaces.

Research in Information Retrieval has progressed against a background of rapidly increasing corpus size and heterogeneity, with every advance in technology quickly followed by a desire to organize and search more unstructured, more heterogeneous, and even bigger corpora. But as retrieval problems get larger and more complicated, evaluating the ranking performance of a retrieval engine gets harder: evaluation requires human judgments of the relevance of documents to queries, and for very large corpora the cost of acquiring these judgments may be insurmountable. This cost limits the types of problems researchers can study as well as the data they can be studied on. We present methods for understanding performance differences between retrieval engines in the presence of missing and noisy relevance judgments. The work introduces a model of the cost of experimentation that incorporates the cost of human judgments as well as the cost of drawing incorrect conclusions about differences between engines in both the training and testing phases of engine development. Through adopting a view of evaluation that is more concerned with distributions over performance differences rather than estimates of absolute performance, the expected cost can be minimized so as to reliably differentiate between engines with less than 1% of the human effort that has been used in past experiments.

Reliable evaluation of Information Retrieval systems requires large amounts of relevance judgments. Making these annotations is quite complex and tedious for many Music Information Retrieval tasks, so performing such evaluations requires too much effort. A low-cost alternative is the application of Minimal Test Collection algorithms, which offer quite reliable results while significantly reducing the annotation effort. The idea is to incrementally select what documents to judge so that we can compute estimates of the effectiveness differences between systems with a certain degree of confidence. In this paper we show a first approach towards its application to the evaluation of the Audio Music Similarity and Retrieval task, run by the annual MIREX evaluation campaign. An analysis with the MIREX 2011 data shows that the judging effort can be reduced to about 35% to obtain results with 95% confidence.

Genetic programming is applied to an information retrieval system in order to improve Boolean query formulation via relevance feedback. This approach brings together the concepts of information retrieval and genetic programming. Documents are viewed as vectors in index term space. A Boolean query, viewed as a parse tree, is an organism in the genetic programming sense. Through the mechanisms of genetic programming, the query is modified in order to improve precision and recall. Relevance feedback is incorporated, in part, via user defined measures over a trial set of documents. The fitness of a candidate query can be expressed directly as a function of the relevance of the retrieved set. Preliminary results based on a testbed are given. The form of the fitness function has a significant effect upon performance and the proper fitness functions take into account relevance based on topicality (and perhaps other factors).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Searching information retrieval systems is a highly interactive, iterative process that cannot be understood simply by comparing the output of a search session (the “search product”) to a query stated in advance. In this article, we examine evaluation goals and methods for studying information retrieval behavior, drawing examples from our own research and that of others. We limit our review to research that employs online monitoring, also known as transaction log analysis. Online monitoring is one of few methods that can capture detailed data on the search process at a reasonable cost; these data can be used to build quantitative models or to support qualitative interpretations of quantitative results. Monitoring is a data collection technique rather than a research design, and can be employed in experimental or field studies, whether alone or combined with other data collection methods. Based on the research questions of interest, the researcher must determine what variables to collect from each data source, which to treat as independent variables to manipulate, and which to treat as dependent variables to observe effects. Studies of searching behavior often treat search task and searcher characteristics as independent variables and may manipulate other independent variables specific to the research questions addressed. Search outcomes, time, and search paths frequently are treated as dependent variables. We discuss each of these sets of variables, illustrating them with sample results from the literature and from our own research. Our examples are drawn from the Science Library Catalog project, a 7-year study of children's searching behavior on an experimental retrieval system. We close with a brief discussion of the implications of these results for the design of information retrieval systems. © 1996 John Wiley & Sons, Inc.

Today, the widespread use of mobile devices allows users to search information "on the go»», whenever and wherever they want, no longer confining Information Retrieval to classic desktop interfaces. We believe that technical advances in Augmented Reality will allow Information Retrieval to go even further, making use of both the users» surroundings and their abilities to interact with the physical world. In this paper, we present the fundamental concept of Reality-Based Information Retrieval, which combines the classic Information Retrieval process with Augmented Reality technologies to provide context-dependent search cues and situated visualizations of the query and the results. With information needs often stemming from real-world experiences, this novel combination has the potential to better support both Just-in-time Information Retrieval and serendipity. Based on extensive literature research, we propose a conceptual framework for Reality-Based Information Retrieval. We illustrate and discuss this framework and present two prototypical implementations, which we tested in small user studies. They demonstrate the feasibility of our concepts and inspired our discussion of notable challenges for further research in this novel and promising area.

Semantic similarity is an essential concept that widen across various fields such as artificial intelligence, natural language processing, information retrieval, relation extraction, document clustering and automatic data extraction. The study proposed in this paper focus on semantic similarity and counter measure on question answering task. Despite the usefulness of semantic similarity in these applications, finding the exact meaning between the words from question and answer pair has become a major challenge in question answering system. To recognize various relationships those exist between question and answer pair, measuring semantically is essential. We present study using a match maker algorithm for similarity measurement between question-answer pair. The study compares similarity measurement using match maker algorithm as against semantic similarity measure on Miller-Charles bench marked data set. This paper present study about different types of algorithm used to measure the semantic similarity between the words and those result compare with match maker algorithm. The experiments on real datasets shows matchmaker algorithm works better than web-based semantic similarity measure.