User involvement and system support in applying search tactics

Information retrieval in a semantic desktop environment is an important aspect that is to be stressed on. There are many works in the past which proposed many improved information retrieval techniques in this environment. But in most of them there is one thing that is lacking, is the involvement of user in the retrieval process. In other words it should be an interactive information retrieval system. This paper proposes an interaction based information retrieval system which interacts with the user to find out the hints and the suggestions from his/her side to get the best of the results which might satisfy the user.

Collaborative Information Retrieval in an information-intensive domain

For years the CHI community has championed the importance of the user in system development. As many of us develop systems, we find that the concept of user involvement is not so easy to implement. Does one always strive to involve the user in the design process? Are there situations when the users should not be involved? What if the user is reluctant to change? How is user involvement handled when the user claims to know all the answers and wants to design the entire interface his or her way? What if the users, or even potential users are not available? How can user involvement be accomplished under these developmental restrictions? User Involvement, therefore, may be a goal - not a given, and how to effect user involvement is not as straight forward as the text books convey! To assist the process of user interface development, many techniques have been developed such as Heuristic Evaluation, Participatory Design, Cognitive Walk Throughs, Task Analysis and Rapid Prototyping. These techniques vary considerably in the extent of user involvement that they require. This panel will attempt to match the technique with the degree of user involvement that the developer is faced with or can achieve. The issues discussed in this session are important to the entire user interface community. Developers will be happy to hear that they are not alone; others have similar problems with users. They will learn which of the techniques are best suited for each development situation. Methodologists will gain greater insight into the breadth and depth of working with, and attempting to satisfy various types of users. They may be able to better refine the technologies we now have available to meet the needs of user interface developers. Members of the audience will be invited to participate as developers and methodologists.

The usage of online search engine is growing rapidly not only in daily life, but also in education. We are interested in understanding what strategies students apply during search, especially the tactics they use to decompose a programming task. In this paper, we report a lab study to investigate students' programming information seeking behavior via Google search engine. Students were given a programming task with limited time, and they were also required to report their online search process including search query and web pages browsed. We analyze the web pages they browsed, model student's behavior, and cluster them into groups with different search tactics. The results show that the web pages they browsed during the task consisted of either conceptual knowledge or coding technical content. The students who performed better would browsed more about conceptual knowledge. Students who set more and smaller unit of subgoals outperformed the students with fewer and larger subgoals.

Indexing and abstracting are like Siamese twins in the information retrieval process. Indexing and abstracting are the two approaches to distilling information content into an abbreviated, but comprehensive representation of an information resource(s). They are knowledge organisation tools which usually provide detailed and accurate maps and road signs in the information superhighway. Digital libraries are characterised by an electronic stock of information which can be accessed via computers, and are extension and augmentations of physical libraries in digital forms. They are information retrieval systems (a device interposed between a potential user of information and the information itself) which provide opportunities to access and retrieve information that is often accessible for a variety of reasons. This chapter presents a literature review on indexing and abstracting, information retrieval process, digital libraries pointing out the importance of indexing and abstracting in the information retrieving process and then highlighting the roles played by indexing and abstracting as tools for information retrieval in digital libraries. The chapter posits that indexing and abstracting plays a significant role as information retrieval tools in digital libraries.

Indexing is an important process in Information Retrieval (IR) systems. It forms the core functionality of the IR process since it is the first step in IR and assists in efficient information retrieval. Indexing reduces the documents to the informative terms contained in them. It provides a mapping from the terms to the respective documents containing them. Once effective index has been built for the collection of documents, retrieval process is simplified. Indexing proceeds at four stages namely content specification, tokenization of documents, processing of document terms, and index building. The index can be stored in the form of different data structures namely direct index, document index, lexicon and inverted index. Index can be built by applying different algorithms or schemes such as single-pass in-memory indexing, blocked-indexing, etc. This paper explains the indexing process with the various data structures and algorithms used for indexing and finally analyses the different indexing approaches with respect to time, memory usage and mean average precision.

When the number of accessible sources of information is so much and even unlimited, the information retrieval process becomes very complicated. Visiting information resources one by one and comparing data or information from all the information sources visited will add much time to the process of rediscovering the information. It takes a technique that can gather information from multiple sources into a single entity to facilitate the process of information retrieval. This study uses 3 e-commerce websites as a source of information. By utilizing the crawling procedure will generate new variables that can store data from the source information, which then these data will be stored in a database. Web crawling works by taking HTML tags as needed, using scrapping techniques. Furthermore, by collecting the data in the database, the information retrieval process can be done easily, and by using the query, this process can be done in less time with 100% recall rate and 93.9 % precision rate.

To satisfy the user information needs, the use of his/her profile is adopted in the information retrieval process. Our goal is to build and exploit the user profile in search process. To achieve this goal, we adopt a Bayesian network as a strategy for personalized search since it provides a clear formalism to model qualitatively and quantitatively information types, namely, a user query, documents, index terms, and user's interests. In this paper, we present a Bayesian network model that captures the relationships between this information to evaluate the relevance as matching relationships between documents, user's interest and query concepts. An experimental evaluation was designed to appraise the exploitation impact of the user profile defined by his/her interests on the search results relevance.

Knowledge-based search tactics are discussed in terms of their role in the functioning of a semantically-based search system for bibliographic information retrieval. This prototype system, EP-X, actively assists users in defining or refining their topics of interest. It does so by applying search tactics to a knowledge-base describing topics in a particular domain and a database describing the contents of individual documents. This paper reviews the empirical studies that lead to the two central concepts implemented in EP-X: Semantically-based search; Knowledge-based search tactics.

The lack of quality in scientific documents affects how documents can be retrieved depending on a user query. Existing search tools for scientific documentation usually retrieve a vast number of documents, of which only a small fraction proves relevant to the user’s query. However, these documents do not always appear at the top of the retrieval process output. This is mainly due to the substantial volume of continuously generated information, which complicates the search and access not properly considering all metadata and content. Regarding document content, the way in which the author structures it and the way the user formulates the query can lead to linguistic differences, potentially resulting in issues of ambiguity between the vocabulary employed by authors and users. In this context, our research aims to address the challenge of evaluating the machine-processing quality of scientific documentation and measure its influence on the processes of indexing and information retrieval. To achieve this objective, we propose a set of indicators and metrics for the construction of the evaluation model. This set of quality indicators have been grouped into three main areas based on the principles of Open Science: accessibility, content, and reproducibility. In this sense, quality is defined as the value that determines whether a document meets the requirements to be retrieved successfully. To prioritize the different indicators, a hierarchical analysis process (AHP) has been carried out with the participation of three referees, obtaining as a result a set of nine weighted indicators. Furthermore, a method to implement the quality model has been designed to support the automatic evaluation of quality and perform the indexing and retrieval process. The impact of quality in the retrieval process has been validated through a case study comprising 120 scientific documents from the field of the computer science discipline and 25 queries, obtaining as a result 21% high, 39% low, and 40% moderate quality.

A study of the use of computing with words in information retrieval processes is presented in this paper. The lack of user-friendly communication interfaces between the user and the system in traditional retrieval approaches is rectified with the use of fuzzy logic at the different levels of the retrieval process. The advent of fuzzy information retrieval systems allows a linguistic representation, providing the user with a tool of understandable language based on words. Apart from outlining the fuzzy linguistic model in the processes of querying and evaluation, some reflections and discussions on the future trends of linguistic modelling in the processes of user feedback and user profile construction are outlined.

We address the problem of designing, implementing, and experimenting with compressed data structures that support rank and select queries over a dictionary of integers. We shine a new light on this classical problem by showing a connection between the input integers and the geometry of a set of points in a Cartesian plane suitably derived from them. We then build upon some results in computational geometry to introduce the first compressed rank/select dictionary based on the idea of “learning” the distribution of such points via proper linear approximations (LA). We therefore call this novel data structure the la_vector . We prove time and space complexities of the la_vector in several scenarios: in the worst case, in the case of input distributions with finite mean and variance, and taking into account the k th order entropy of some of its building blocks. We also discuss improved hybrid data structures, namely, ones that suitably orchestrate known compressed rank/select dictionaries with the la_vector . We corroborate our theoretical results with a large set of experiments over datasets originating from a variety of applications (Web search, DNA sequencing, information retrieval, and natural language processing) and show that our approach provides new interesting space-time tradeoffs with respect to many well-established compressed rank/select dictionary implementations. In particular, we show that our select is the fastest, and our rank is on the space-time Pareto frontier.

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.

Recently, Temporal Information Retrieval (TIR) has grabbed the major attention of the information retrieval community. TIR exploits the temporal dynamics in the information retrieval process and harnesses both textual relevance and temporal relevance to fulfill the temporal information requirements of a user Ur Rehman Khan et al., 2018. The focus time of document is an important temporal aspect which is defined as the time to which the content of the document refers Jatowt et al., 2015; Jatowt et al., 2013; Morbidoni et al., 2018, Khan et al., 2018. To the best of our knowledge, there does not exist any standard benchmark data set (publicly available) that holds the potential to comprehensively evaluate the performance of focus time assessment strategies. Considering these aspects, we have produced the Event-dataset, which is comprised of 35 queries and set of news articles for each query. Such that, C={Qs,Ds}, where C represents the dataset, Qsis query set Qs={q1,q2,q3,…….,q35}and for each qi there is a set of news articles qi={dr,dnr}. dr,dnrare sets of relevant documents and non-relevant documents respectively. Each query in the dataset represents a popular event. To annotate these articles into relevant and non-relevant, we have employed a user-study based evaluation method wherein a group of postgraduate students manually annotate the articles into the aforementioned categories. We believe that the generation of such dataset can provide an opportunity for the information retrieval researchers to use it as a benchmark to evaluate focus time assessment methods specifically and information retrieval methods generically.

Several deep supervised hashing techniques have been proposed to allow for querying large image databases. However, it is often overlooked that the process of information retrieval can be modeled using information-theoretic metrics, leading to optimizing various proxies for the problem at hand instead. Contrary to this, we propose a deep supervised hashing algorithm that optimizes the learned codes using an information-theoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of large-scale hashing and information retrieval leading to a novel information-theoretic measure, the Quadratic Spherical Mutual Information (QSMI), that is inspired by QMI, but leads to significant better retrieval precision. Indeed, the effectiveness of the proposed method is demonstrated under several different scenarios, using different datasets and network architectures, outperforming existing deep supervised image hashing techniques.