A computerized system for the organized retrieval of life history information

Image retrieval: Benchmarking visual information indexing and retrieval systems

When using Information Retrieval (IR) systems, users often present search queries made of ad-hoc keywords. It is then up to the information retrieval systems (IRS) to obtain a precise representation of the user's information need and the context (preferences) of the information. To address this problem, we investigate optimization of IRS to individual information needs in order of relevance. The goal of this article is to develop algorithms that optimize the ranking of documents retrieved from IRS according to user search context. In particular, the ranking task that led the user to engage in information-seeking behaviour during search tasks. This article discusses and describes a Document Ranking Optimization (DROPT) algorithm for IR in an Internet-based or designated databases environment. Conversely, as the volume of information available online and in designated databases is growing continuously, ranking algorithms can play a major role in the context of search results. In this article, a DROPT technique for documents retrieved from a corpus is developed with respect to document index keywords and the query vectors. This is based on calculating the weight (w ij ) of keywords in the document index vector, calculated as a function of the frequency of a keyword k j across a document. The purpose of the DROPT technique is to reflect how human users can judge the context changes in IR result rankings according to information relevance. This article shows that it is possible for the DROPT technique to overcome some of the limitations of existing traditional (tƒ × idƒ) algorithms via adaptation. The empirical evaluation using metrics measures on the DROPT technique carried out through human user interaction shows improvement over the traditional relevance feedback technique to demonstrate improving IR effectiveness.

By examining the references of research and specific topic articles of JASIST, this study explored the disciplines and subjects relating to information science. According to the website of JASIST, there are 1,341 research and specific topic articles with 51,359 references during the period of 1998 to 2008. Since journal articles and monographs are cited by JASIST most, the citation analysis of this study will focus on these two document type references only. Firstly, this study applies the Bradford's Law and Bradford-Zipf's Law to identify the core journals which were cited by JASIST. Then, search the classification number and subject categories of WorldCat and Ulrichsweb.com and also the descriptors of LISA to analyze cited references. Followings are the research results: 一、JASIS(T) published 2,031 articles from 1998 to 2008, with an average number of 185 per year. Among them, 1,341articles are research and specific topic paper, contributing 66.02% of all published items. Journal articles and monographs are two most cited reference for research and specific topic articles. 二、JASIS(T) has cited a total of 27,115 journal literature, distributing over 2,994 journals. By applying the Bradford's Law, there are four core journals cited by JASIS(T). However, by applying the Bradford-Zipf's Law, there appears to be ten core journals. The journal that had been cited the most is JASIS(T) itself (17.47% of all citations), suggesting JASIS(T)'s self-citation is obvious. 三、For journals cited by the JASIS(T), bibliography. library science. information resources (general), science, and social sciences are the three most cited disciplines. The most commonly cited subjects, which identified from WorldCat and Ulrichsweb.com, are information science, information technology, information storage and retrieval systems, library science, and science. However, searching, online information retrieval, information work, subject indexing, and information storage and retrieval, which searched from LISA, are the most cited descriptors of the library and information science journals cited by JASIS(T). 四、JASIS(T) has cited a total of 27,115 monograph literature, distributing over 5,565 books. Introduction to Modern Information Retrieval written by Salton & McGill was cited the most. Three of the top ten cited monographs are written by Salton. It may suggest that Salton is one of the most influential authors in the field of information science. In addition, 91% of the books cited by JASIS(T) less than 3 times indicates that JASIS(T) cited monographs diversified. 五、For monographs cited by the JASIS(T), science, social sciences, and bibliography. library science. information resources (general) are the three most cited disciplines. The most commonly cited subjects, which identified from WorldCat and Ulrichsweb.com, are information storage and retrieval systems, human-computer interaction, information retrieval, information science, and cognition. 六、From comprehensive analysis on the discipline of the journals and monographs cited by JASIS(T), it can be found that bibliography. library science. information resources (general), science, and social sciences are the most cited disciplines by JASIS(T). In the other words, these three disciplines are not only the most influential resources, but also are closely related to information science.

In the internet era, search engines play a vital role in information retrieval from web pages. Search engines arrange the retrieved results using various ranking algorithms. Additionally, retrieval is based on statistical searching techniques or content-based information extraction methods. It is still difficult for the user to understand the abstract details of every web page unless the user opens it separately to view the web content. This key point provided the motivation to propose and display an ontology-based object-attribute-value (O-A-V) information extraction system as a web model that acts as a user dictionary to refine the search keywords in the query for subsequent attempts. This first model is evaluated using various natural language processing (NLP) queries given as English sentences. Additionally, image search engines, such as Google Images, use content-based image information extraction and retrieval of web pages against the user query. To minimize the semantic gap between the image retrieval results and the expected user results, the domain ontology is built using image descriptions. The second proposed model initially examines natural language user queries using an NLP parser algorithm that will identify the subject-predicate-object (S-P-O) for the query. S-P-O extraction is an extended idea from the ontology-based O-A-V web model. Using this S-P-O extraction and considering the complex nature of writing SPARQL protocol and RDF query language (SPARQL) from the user point of view, the SPARQL auto query generation module is proposed, and it will auto generate the SPARQL query. Then, the query is deployed on the ontology, and images are retrieved based on the auto-generated SPARQL query. With the proposed methodology above, this paper seeks answers to following two questions. First, how to combine the use of domain ontology and semantics to improve information retrieval and user experience? Second, does this new unified framework improve the standard information retrieval systems? To answer these questions, a document retrieval system and an image retrieval system were built to test our proposed framework. The web document retrieval was tested against three key-words/bag-of-words models and a semantic ontology model. Image retrieval was tested on IAPR TC-12 benchmark dataset. The precision, recall and accuracy results were then compared against standard information retrieval systems using TREC_EVAL. The results indicated improvements over the standard systems. A controlled experiment was performed by test subjects querying the retrieval system in the absence and presence of our proposed framework. The queries were measured using two metrics, time and click-count. Comparisons were made on the retrieval performed with and without our proposed framework. The results were encouraging.

The accuracy of ad-hoc document retrieval systems has plateaued in the last few years. At DFKI, we are working on so-called collaborative information retrieval (CIR) systems which unobtrusively learn from their users’ search processes. We focus on a restricted setting in CIR in which only old queries and correct answer documents to these queries are available for improving a new query. For this restricted setting we propose new approaches for query expansion procedures. This paper describes query expansion methods to be used in collaborative information retrieval. We define collaborative information retrieval as a task, where an information retrieval system uses information gathered from previous search processes from one or several users to improve retrieval performance for the current user searching for information. We show how collaboration of individual users can improve overall information retrieval performance. Performance in this case is expressed in terms of quality and utility of the retrieved information regardless of specific user groups.

The design of information retrieval (IR) systems must respond to the goals, intentionality and prior domain knowledge of the users. During focus groups conducted, end users complained that instead of looking for specific‐items, they might be interested in a spectrum of concepts – all things related to something or all things of a particular color. To respond to these needs, an entirely visuals‐driven information retrieval system project was developed using a test‐bed of copyright‐free images reflecting monographs, graphics, and work collections. In the absence of such IR systems, not much is known about how users will interact with a visuals‐only retrieval system. This poster describes the project in general and its usage to explore (a) how users interact with graphic‐only retrieval for exploring traditional and non‐traditional access points and (b) how the affective component impacts the use of such systems. Findings based on the study will help shed light on research based on visual information systems and user behavior when interacting with such systems. The findings will be useful both in designing systems that respond to user needs, and add to prior research in information seeking and retrieval.

The SMART System and Okapi System are two distinguished information retrieval (IR) systems. The former uses a vector space model and the latter uses a probabilistic retrieval strategy. A lot of previous research on information retrieval was carried out on these two systems, which showed that both were effective in IR. In this paper, we carry out a series of experiments on TREC-9 collections to discuss the different performance between these two kinds of retrieval systems in Chinese information retrieval. Our experimental results show that the performance of the Okapi System is better than that of the SMART System.

Purpose – The purpose of this paper is to propose a method to have more accurate results in comparing performance of the paired information retrieval (IR) systems with reference to the current method, which is based on the mean effectiveness scores of the systems across a set of identified topics/queries. Design/methodology/approach – Based on the proposed approach, instead of the classic method of using a set of topic scores, the documents level scores are considered as the evaluation unit. These document scores are the defined document’s weight, which play the role of the mean average precision (MAP) score of the systems as a significance test’s statics. The experiments were conducted using the TREC 9 Web track collection. Findings – The p-values generated through the two types of significance tests, namely the Student’s t-test and Mann-Whitney show that by using the document level scores as an evaluation unit, the difference between IR systems is more significant compared with utilizing topic scores. Originality/value – Utilizing a suitable test collection is a primary prerequisite for IR systems comparative evaluation. However, in addition to reusable test collections, having an accurate statistical testing is a necessity for these evaluations. The findings of this study will assist IR researchers to evaluate their retrieval systems and algorithms more accurately.

Music bibliographers and librarians are accustomed to an essential dichotomy that presents itself in the ordering of musical documents for retrieval. The documents themselves (scores and recordings, mostly) must be represented in retrieval systems according to their physical characteristics (that is, according to formatted physical descriptions) for the purposes of general bibliographic control; but the musical works that are contained in and conveyed by these documents require representation according to their intellectual origins and musical characteristics (that is, author and uniform title headings). Collections of musical documents are unique among collections of documents (in libraries, bibliographies, etc.) in that the influence of repertory is such that a given collection will have many instantiations of the same musical work--one Tchaikovsky Fifth Symphony (a musical work), but a dozen scores of different sizes and formats, and dozens of recordings, not to mention excerpts and arrangements. Thus the intellectual control of musical works assumes tremendous importance in the work of music librarians and bibliographers. At the beginning of this new century we find a rapidly evolving domain for research and very pragmatic implementation known as Music Information Retrieval (MIR). Music Information Retrieval is basically the activity of automating the retrieval of musical works, or parts of musical works. MIR embraces everything from query-by-humming systems that allow a searcher to hum a tune for which the database returns an audio output, to the design of metadata structures and standard name-title-subject querying of bibliographic databases. The field encompasses various system-Engineering components, such as audioinformation retrieval (AIR), and it also encompasses musicological engineering problems such as recognition of parts of musical works. A major component is the creation of digital music libraries--electronic repositories of musical works. We will look more closely at the components of MIR later, but for now it is important to comprehend the evolution of this new domain for music librarianship and bibliography. In information retrieval in general, as well as in music information retrieval, the objects or that will be mapped into databases for retrieval must be well-defined. An entity is simply a thing--it might be a physical characteristic or it might be a concept. Entities occupy prominent positions in the structure of information retrieval databases. Consider an online catalog: therein things such as tides, composers' names, tide-page transcriptions, etc., could all be considered entities. Entities have attributes (inherent characteristics that can be defined) that are used to structure their representations in information retrieval systems. In music information retrieval, as we will see below, there are many potential entities at play. But universal to the retrieval of music information--whether it be bibliographic or audiographic using surrogates or real-time sound--is the concept of the musical work. Musical works, then, as opposed to musical documents such as scores or recordings of musical works, form a key entity for music information retrieval. Ultimately, searches for a given musical work rely on the hope of subsequent selection of instantiation in one of several documentary formats. Musical works have been variously and industriously described by musicologists and music bibliographers. Yet, in the information retrieval domain, the work as opposed to the document has only recently received focused attention. (1) Efforts to define works as information retrieval entities and to document their occurrence empirically are quite recent. In fact, systems for bibliographic information retrieval, and more recently for information storage and retrieval, have been designed with the document as the key entity, and works have been dismissed as too abstract or difficult to define empirically to take a role in information retrieval. …

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality. New more holistic directions in CIR are emerging that conceptualize human-IR system interaction at the human-computer interaction level (taking into account interface issues), the information behavior level (taking into account the role of IR system interaction in the total range of people’s information behaviors), and the

People with disabilities face barriers when engaging with information retrieval (IR) systems due to designs that overlook their needs. This systematic literature review explores research for individuals with disabilities interacting with IR systems. Relevant theories concerning disabilities were examined, and the gap model was used as the theoretical framework that guided the review. This review covers relevant research published from 2000 to 2023, focusing on user groups with sensory, cognitive, and motor impairments. The main topics are help‐seeking situations encountered by these user groups in various IR systems due to system design not meeting user needs, and search tactics applied by users with different types of disabilities corresponding to various help‐seeking situations. Design recommendations for IR systems and platforms were also examined. Key limitations in existing research and the authors' reflections are highlighted, including a lack of theories on the interactions between people with disabilities and IR systems, imbalanced research on and misclassification between different types of impairments, unclear distinctions between accessibility and usability, unexplored IR issues in mobile environments, and inadequate existing IR system designs, along with the challenges posed by one‐size‐fits‐all design. Further research opportunities are also proposed.

The ideal remote access, on‐line, information retrieval and display system would exploit the inherent potential of on‐line program capabilities to a far greater extent than those currently employed by on‐going, on‐line information systems. Special emphasis would be placed on the utilization of the feedback capabilities of on‐line user‐computer interactions as a means of increasing the effectiveness and efficiency of search and display capabilities. The most significant contribution of online program capabilities to the advancement of information retrieval is their ability to top the immense computational capacities of the computer as a retrieval tool. The ideal on‐line information retrieval system would offer statistical inference analysis and heuristic optimization programs as aids to query formulation. In fact, the ideal system would include on‐line programs capable of automatically computing, generating, and executing, optimal query formulations and retrieval on request. Implementation of expanded on‐line capabilities to achieve the ideal on‐line information retrieval and display system will have a considerable economic impact on the entire field of information science.

Introduction:The effective performance of an Information Retrieval System depends on the quality with which the organization of information is performed, which will imply a retrieval of the most relevant and pertinent information, since these procedures are conditioned to each other, creating a bridge between input and output of information. Objective:To evaluate the impact of the exhaustiveness and specificity variables and the recall and precision measures, as well as the concepts of relevance and pertinence, in Information Retrieval Systems. Methodology:It is characterized as a descriptive and exploratory study, based on a narrative literature review aiming to present the different concepts, their converging and divergent points. Results:As a contribution, we present a proposal for a flow for an Information Storage and Retrieval System, centered on the user, bringing together several aspects related to measures of recall and precision, of relevance and pertinence.Conclusion:It is considered as the final contribution of this study to highlight the importance of a systemic view, in which all elements of an Information Storage and Retrieval System are in interrelation, having the user as the main element; and present the fundamental activities that are important for the training of professionals able to build consistent Systems.

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution-based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques. © 1995 John Wiley & Sons, Inc.

We welcome you to PaIR 09, the 2nd workshop on patent information retrieval, organised by the Information Retrieval Facility (IRF) and Matrixware Information Services. This second edition of our workshop extends and continues topics covered in 2008, but also addresses new interesting subjects. Patent Information Retrieval is an active and challenging field both for researchers and for professional information specialists. Patents play a key role not only in protecting intellectual property but also as a strategic business factor in all modern economies. Despite the enormous advances in Information Retrieval techniques in the past few years, advanced search tools for patent professionals are still in their infancy, so the research in patent retrieval represents an important opportunity for research. Patent search is a particular challenge to information retrieval and access systems. Amongst the challenges successful patent search of search system of the future will have to face are; very large numbers of highly complex structured documents;highly heterogeneous document collections (scientific papers, legal public disclosure as well as patents);multiple languages;ambiguous and conflicting jargon; complex languagetechnological concepts;sophisticated legal jargon;ranges and other complex query formstracking temporal issues like publication datatabular and graphical information mixed into text;and so on. The objective of the workshop is to provide a forum for Information Retrieval and Knowledge Management scientists as well as Patent Retrieval experts from industry to study the next generation of patent search tools. This year the workshop received 13 submissions, of which it accepted 5 full papers. An additional 6 papers were invited to do a short presentation and display posters, in order to trigger interesting discussions on future directions of Patent Information Retrieval. The 5 full papers cover some of the most salient aspects of Patent IR. Phrase-based Document Categorization Revisited (Koster and Beney) addresses the problem of whether or not the use of linguistic techniques improves patent classification. In Identification of Low/High Retrievable Patents using Content-Based Features, Bashir and Rauber analyse the factors that lead to bias in retrieval systems in order to attempt to compensate and make all documents 'retrievable'. Yang and colleagues (A Design Rationale Representation Model using Patent Documents) show us how to model and extract specific information (i.e. design rationales) from patent documents. In a similar line of thought, Tiwana and Horowitz (Extracting Problem Solved Concepts from Patent Documents) work on extracting the essence of a patent: the problem being solved by that specific invention, in order to improve future prior art searches. Further improvements in patent searches may be obtained if one follows the ideas suggested by Klampanos and colleagues in A Case for Probabilistic Logic for Scalable Patent Retrieval. In addition to the 5 full papers, 6 research groups will describe their ideas in a booster session, followed by poster presentations during the workshop's break. The topics covered by these papers tackle patent classification and categorisation, translation, natural language processing and, of course, new ways of performing prior art search. We expect that these papers will trigger interesting conversations and future development in IP search, through a closer collaboration between researchers and industry representatives. We also hope that the workshop will be a springboard for many future events and lead to the recognition of patent searching as one of the central areas of research in Information Retrieval and Knowledge Management.