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This study surveyed the membership of the Ohio Prospect Research Network to ascertain where prospect researchers get most of the information necessary to perform their jobs. It was hypothesized that prospect researchers do not utilize the library, even when the same services or information may be found for free at the library. A questionnaire was developed to ascertain methods of obtaining information for prospect research. The first part of the survey explored library usage by asking how often members visited libraries, types of libraries visited, and what factors influenced their decision to choose a library over other methods of obtaining information. The second part of the survey consisted of a list of specific resources commonly used in prospect research. Respondents were asked where they were most likely to access those sources. The last part of the survey listed common research functions performed by prospect researchers, and respondents were asked which type of resource was most useful when performing those functions. Analysis of the information-seeking functions commonly performed by prospect researchers shows that the majority of information needs may be met with the combined use of sources available in-house (print, online services, and Internet) without having to utilize the library. Data shows that prospect researchers rarely consult with librarians. A copy of the questionnaire is appended. (Contains 13 references and 20 notes.) (Author/MES)

The Internet is a network of networks which continually accumulates and amasses information, much of which is without organization and evaluation. This study addresses the need for establishing a database of Uniform Resource Locators (URLs), and for collecting, organizing, indexing, and publishing catalogs of URLs. Librarians and information professionals were asked via a World Wide Web survey to list and evaluate the URLs they most frequently use to fill information needs for their information-seeking communities. The survey achieved a response rate of 86.4% (n=70). Librarians and information professionals are incorporating URLs into the repertoire of sources they use to provide information. Projects involving construction of catalogs of URLs designed by librarians and information professionals are in the formative stages, with 44.4% of study participants having an informal collection of URLs maintained by bookmarking. The need for a listing of URLs is affirmed by the fact that 80% of participants requested a listing of the URLs resulting from the study. Appendices include the cover letter, survey, and a listing of URLs frequently accessed by surveyed librarians. (Contains 19 references.) (Author/SWC)

This census population study surveyed 147 occupational safety and health professionals employed by the Ohio Division of Safety and Hygiene to determine how they satisfy their work-related information needs. The study achieved a response rate of 81% (n=120). Respondents were asked to identify the information providers whom they had consulted in a recent major decision or planning situation. Variables studied included information providers used, levels of satisfaction with providers, barriers to accessing information, and demographic variables. Respondents were also surveyed on their reason for use and non-use of the Bureau of Workers Compensation, Division of Safety and Hygiene Library (BWC Library). The study identifies the information-seeking behaviors of Division safety professionals, providing insight into their concerns about the cost, quality, usefulness, and time aspects of information. Division personnel use the BWC Library extensively for their information needs, and chose library materials as one of the three most helpful information sources. Safety professionals sought information providers that were close by and familiar, whether print material or a person, and then turned to outside sources. Over 70% stated they used a most helpful source based on previous experience. Appendices include respondents' written answers and comments, cover letters, and the survey. (Contains 13 references.) (Author/SWC)

This study examines research that is part of an alternative paradigm in information needs and uses assessment. Approaches in the alternative paradigm include Sense-Making, Value-Added, and Anomalous States of Knowledge. From an analysis of these approaches and related research, a conceptual model has been developed. The model focuses on several elements that should guide the design and implementation of information systems: (1) the information system should be user-oriented (oriented toward the individual or group with the information need); (2) information needs should be determined as they relate to users in particular situations or contexts; and (3) in information systems mediated by intermediaries, the success of the user-intermediary interaction is of prime importance in determining information need. The model also focuses toward the development of a theory of relevance that considers qualitative and contextual judgements. (Contains 51 references.) (Author/JLB)

Information retrieval (IR) systems have tremendously broaden users' access to information. However, users need to select their needs from trillions of information indexed daily. Due to the "semantic gap" between queries and indexed terms in IR system, whether users can satisfy their needs depends on whether they use the correct terms as queries. Recommender systems have become a counterpart of information retrieval systems such as Google. They do not require users to specify their information needs in advance. They model users' preferences based on their history data, and automatically recommend items which satisfy their needs. In this way, both semantic gap and information overload can be alleviated. Collaborative filtering is the most popular recommendation algorithm used in academia and industry. However, it suffers from the cold start problem, where it cannot recommend new items to users or give recommendation to new users. Its recommendations also bias towards popular items. The goal of this thesis is to develop recommendation algorithms which can solve the item-side and user-side cold-start problems. We incorporate item content to solve the item-side cold start problem, and incorporate user similarity networks or social networks to solve the user-side cold-start problem. Our contributions are listed as follows. First, we develop a content-based movie recommendation algorithm which incorporates user, actor, and movie into the same model. Second, we develop a content-based music recommendation algorithm. The core component is a hierarchical dynamic programming algorithm to compute music similarities. The resulting music similarity matrix is then employed to make recommendations. Third, we apply social recommendation algorithms on Douban dataset. We look into a "Twitter-like" user-follower social network to see if it can improve recommendation performance. Finally, we validate the existence of social influence. We also analyze the social influence by investigating the correlation between one's influence and centrality, and by investigating how recommendation effectiveness changes with the number of recommendations. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The tremendously huge volume of biomedical literature, scientists' specific information needs, long terms of multiples words, and fundamental problems of synonym and polysemy have been challenging issues facing the biomedical information retrieval community researchers. Search engines have significantly improved the efficiency and effectiveness of biomedical literature searching. The search engines, however, are known to return many results that are irrelevant to the intention of a user's query, in other words, perform not very sound in terms of precision and recall. To further improve precision and recall of biomedical informational retrieval, various query expansion strategies are widely used. In this thesis, we concentrate on empirical comparison, experiments and evaluations in investigating query expansion methods. We also use the findings as an empirical justification for cluster-based query expansion. We have investigated broadly many methods of query expansion such as local analysis, global analysis, ontology-based term reweighting across various search engines and obtained important insights. Among the findings, two-stage concept-based latent semantic analysis strategy and cluster-based query expansion have been presented and the Singular Value Decomposition (SVD) technique in the Latent Semantic Indexing (LSI) is utilized in the proposed method. In contrast to other query expansion methods, our strategy selects those terms that are most similar to the concepts of in the query as well as the related documents, rather than selects terms that are similar to the query terms only. Furthermore, we propose a novel framework for cluster-based query expansion. we have designed and implemented a novel and efficient computational approach to cluster-based query expansion using language modeling. Through our experiments in TREC genomic track ad-hoc retrieval task, we demonstrate that clusters which are created based on the whole collection or the initially returned document results of the original query can be utilized to perform query expansion and eventually improve the overall effectiveness and performance of information retrieval system in the biomedical literature retrieval. Lastly, we believe the principles of this strategy may be extended and utilized in other domains. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The objective of this work is to examine the efficacy of natural language processing (NLP) in summarizing bibliographic text for multiple purposes. Researchers have noted the accelerating growth of bibliographic databases. Information seekers using traditional information retrieval techniques when searching large bibliographic databases are often overwhelmed by excessive, irrelevant data. Scientists have applied natural language processing technologies to improve retrieval. Text summarization, a natural language processing approach, simplifies bibliographic data while filtering it to address a user's need. Traditional text summarization can necessitate the use of multiple software applications to accommodate diverse processing refinements known as "points-of-view." A new, statistical approach to text summarization can transform this process. Combo, a statistical algorithm comprised of three individual metrics, determines which elements within input data are relevant to a user's specified information need, thus enabling a single software application to summarize text for many points-of-view. In this dissertation, I describe this algorithm, and the research process used in developing and testing it. Four studies comprised the research process. The goal of the first study was to create a conventional schema accommodating a genetic disease etiology point-of-view, and an evaluative reference standard. This was accomplished through simulating the task of secondary genetic database curation. The second study addressed the development and initial evaluation of the algorithm, comparing its performance to the conventional schema using the previously established reference standard, again within the task of secondary genetic database curation. The third and fourth studies evaluated the algorithm's performance in accommodating additional points-of-view in a simulated clinical decision support task. The third study explored prevention, while the fourth evaluated performance for prevention and drug treatment, comparing results to a conventional treatment schema's output. Both summarization methods identified data that were salient to their tasks. The conventional genetic disease etiology and treatment schemas located salient information for database curation and decision support, respectively. The Combo algorithm located salient genetic disease etiology, treatment, and prevention data, for the associated tasks. Dynamic text summarization could potentially serve additional purposes, such as consumer health information delivery, systematic review creation, and primary research. This technology may benefit many user groups. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

An unprecedented amount of information encompassing almost every facet of human activities across the world is generated daily in the form of zeros and ones, and that is often the only form in which such information is recorded. A good fraction of this information needs to be preserved for periods of time ranging from a few years to centuries. Consequently, the problem of preserving digital information over a long-term has attracted the attention of many organizations, including libraries, government agencies, scientific communities, and individual researchers. In this dissertation, we address three issues that are critical to ensure long-term information preservation and access. The first concerns the core requirement of how to guarantee the integrity of preserved contents. Digital information is in general very fragile because of the many ways errors can be introduced, such as errors introduced because of hardware and media degradation, hardware and software malfunction, operational errors, security breaches, and malicious alterations. To address this problem, we develop a new approach based on efficient and rigorous cryptographic techniques, which will guarantee the integrity of preserved contents with extremely high probability even in the presence of malicious attacks. Our prototype implementation of this approach has been deployed and actively used in the past years in several organizations, including the San Diego Super Computer Center, the Chronopolis Consortium, North Carolina State University, and more recently the Government Printing Office. Second, we consider another crucial component in any preservation system--searching and locating information. The ever-growing size of a long-term archive and the temporality of each preserved item introduce a new set of challenges to providing a fast retrieval of content based on a temporal query. The widely-used cataloguing scheme has serious scalability problems. The standard full-text search approach has serious limitations since it does not deal appropriately with the temporal dimension, and, in particular, is incapable of performing relevancy scoring according to the temporal context. To address these problems, we introduce two types of indexing schemes--a location indexing scheme, and a full-text search indexing scheme. Our location indexing scheme provides optimal operations for inserting and locating a specific version of a preserved item given an item ID and a time point, and our full-text search indexing scheme efficiently handles the scalability problem, supporting relevancy scoring within the temporal context at the same time. Finally, we address the problem of organizing inter-related data, so that future accesses and data exploration can be quickly performed. We, in particular, consider web contents, where we combine a link-analysis scheme with a graph partitioning scheme to put together more closely related contents in the same standard web archive container. We conduct experiments that simulate random browsing of preserved contents, and show that our data organization scheme greatly minimizes the number of containers needed to be accessed for a random browsing session. Our schemes have been tested against real-world data of significant scale, and validated through extensive empirical evaluations. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors and contextual factors. The current study looks particularly at users' dwelling behavior, measured by the time that they spend on documents; and several contextual factors: the stage of users' work tasks, task type, users' knowledge of task topics, to explore whether or not taking account of task stage, task type, and topic knowledge could help predict document usefulness from the time that users spend on the documents. This study also investigates whether or not expanding queries with important terms extracted from task products and useful pages improves search performance. To these ends, a controlled lab experiment was conducted with 24 student participants, each coming three times in a two-week period to work on three sub-tasks in a general work task. Data were collected by logging software that recorded user-system interaction and questionnaires that elicited users' background information and perceptions on a number of aspects. Observations in the study and examinations of the data found that the time users spent on documents could have three different types: total display time, total dwell time, and decision time, which had different roles in working as a reliable indicator of document usefulness. Task stage was found to help interpret certain types of time as reliable indicators of document usefulness in certain task types, so was topic knowledge, and the latter played a more significant role when both were available. This study contributes to a better understanding of how information seeking behaviors, specifically, time that users spend on documents, can be used as implicit evidence of document usefulness, as well as how contextual factors of task stage, topic knowledge, and task type can help interpret time as an indicator of usefulness. These findings have theoretical and practical implications for using behaviors and contextual factors in the development of personalization systems. Future studies are suggested on making use of these findings as well as research on related issues. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The variety and complexity of potentially-related data resources available for querying--webpages, databases, data warehouses--has been growing ever more rapidly. There is a growing need to pose integrative queries "across" multiple such sources, exploiting foreign keys and other means of interlinking data to merge information from diverse sources. This has traditionally been the focus of research within Information Extraction (IE) and Information Integration (II) communities, with IE focusing on converting unstructured sources into structured sources, and II focusing on providing a unified view of diverse structured data sources. However, most of the current IE and II methods, which can potentially be applied to the problem of integration across sources, require large amounts of human supervision, often in the form of annotated data. This need for extensive supervision makes existing methods expensive to deploy and difficult to maintain. In this thesis, we develop techniques that generalize from limited human input, via weakly-supervised methods for IE and II. In particular, we argue that "graph-based representation of data and learning over such graphs can result in effective and scalable methods for large-scale Information Extraction and Integration." Within IE, we focus on the problem of assigning semantic classes to entities. First we develop a context pattern induction method to extend small initial entity lists of various semantic classes. We also demonstrate that features derived from such extended entity lists can significantly improve performance of state-of-the-art discriminative taggers. The output of pattern-based class-instance extractors is often high-precision and low-recall in nature, which is inadequate for many real world applications. We use Adsorption, a graph based label propagation algorithm, to significantly increase recall of an initial high-precision, low-recall pattern-based extractor by combining evidences from unstructured and structured text corpora. Building on Adsorption, we propose a new label propagation algorithm, Modified Adsorption (MAD), and demonstrate its effectiveness on various real-world datasets. Additionally, we also show how class-instance acquisition performance in the graph-based SSL setting can be improved by incorporating additional semantic constraints available in independently developed knowledge bases. Within Information Integration, we develop a novel system, Q, which draws ideas from machine learning and databases to help a non-expert user construct data-integrating queries based on keywords (across databases) and interactive feedback on answers. We also present an "information need-driven" strategy for automatically incorporating new sources and their information in Q. We also demonstrate that Q's learning strategy is highly effective in combining the outputs of "black box" schema matchers and in re-weighting bad alignments. This removes the need to develop an expensive mediated schema which has been necessary for most previous systems. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Search engines have now become essential tools in all aspects of our life. Although a variety of information needs can be served very successfully, there are still a lot of queries that search engines can not answer very effectively and these queries always make users feel frustrated. Since it is quite often that users encounter such "difficult queries," improving Web search for these queries can bring significant benefits to users. However, the problem has so far been under-addressed. In this dissertation, I propose to systematically study this problem from different perspectives, naturally corresponding to different stages of an interactive search process. Specifically, I propose to improve search quality for difficult queries by: (1) Effective query reformulation, i.e., improving a search engine in the stage of query formulation. A query is difficult because it does not contain the right keywords or lacks discriminative keywords. A better formulation of query by addressing vocabulary mismatch or improving discrimination can lead to better results. (2) User-oriented search result organization, i.e., improving a search engine in result presentation. Ambiguous queries are difficult and often lead to search results with mixed senses. Search result organization can make search results easily accessible for users. (3) Incorporating user negative feedback, i.e., improving a search engine by learning from user interactions. When a query is extremely difficult and all the top results (e.g., top 10) are totally irrelevant, the feedback that a user can provide would be solely negative. I propose to develop effective negative relevance feedback strategies to improve the ranking accuracy of the next few pages when the user clicks on the "Next" button. (4) Supporting effective browsing, i.e., allowing a user to find information without queries. Browsing is a complementary mechanism to querying and is especially important for difficult queries. I propose to construct a novel multi-resolution topic map to enable effective browsing beyond hyperlinks. In summary, my dissertation is to study how to improve search engines for difficult queries along all these four directions by exploiting both "global massive search logs" and "immediate user feedback" information. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]