Increasingly, workplace information seeking takes place in digital information environments and is reliant upon search systems. Existing systems are designed to retrieve information that is relevant to the query, but are not capable of identifying information that is well-suited to the context and situation of a search. This is a problem for professionals who often are searching for a small amount of useful information that can be applied to a problem or task, and have limited time to browse through large sets of results. This inability of search systems to discriminate between relevant and useful documents is one of the core problems in information retrieval. In this dissertation, I address this problem by studying the role that contextual factors play in determining how a group of professionals searches for and selects information. The central question concerns the nature of the relationships between these contextual factors, specifically between the genres in the document collection and the tasks of the searcher, with an aim to exploit such relationships to improve workplace information retrieval. Research was conducted through multiple studies in three phases, moving from an exploratory study of workplace information behaviour to a controlled experimental user study. Findings confirm that workplace context shapes search behaviour. This relationship is modeled as a set of key contextual factors and sets of context-dependent access constraints, preferred document characteristics, and search strategies. Among the contextual factors identified, work tasks and information tasks were found to be significantly associated with document genres. This task-genre relationship was modeled as a matrix of associations between domain-specific task and genre taxonomies and successfully implemented as a filtering component in a workplace search system. This is the first major study of the relationship between task and genre in information seeking and of its application to information retrieval systems.

Structured document retrieval (SDR) systems minimize the effort users spend to locate relevant information by retrieving sub-documents (i.e., parts of, as opposed to entire, documents) to focus the user's attention on the relevant parts of a retrieved document. SDR search tasks are differentiated by the multiplicity of ways that users prefer to spend effort and gain relevant information in SDR. The sub-document retrieval paradigm has required researchers to undertake costly user studies to validate whether new IR measures, based on gain and effort, accurately capture IR performance. We propose the Extended Structural Relevance (ESR) framework as a way, akin to classical set-based measures, to formulate SDR measures that share the common basis of our proposed pillars of SDR evaluation: relevance, navigation and redundancy. Our experimental results show how ESR provides a flexible way to formulate measures, and addresses the challenge of testing measures across related search tasks by replacing costly user studies with low-cost simulation.

Inter-document similarity is used for clustering, classification, and other purposes within information retrieval. In this thesis, we investigate several aspects of document similarity. In particular, we investigate the quality of several measures of inter-document similarity, providing a framework suitable for measuring and comparing the effectiveness of inter-document similarity measures. We also explore areas of research related to novelty and diversity in information retrieval. The goal of diversity and novelty is to be able to satisfy as many users as possible while simultaneously minimizing or eliminating duplicate and redundant information from search results. In order to evaluate the effectiveness of diversity-aware retrieval functions, user query logs and other information captured from user interactions with commercial search engines are mined and analyzed in order to uncover various informational aspects underlying queries, which are known as subtopics. We investigate the suitability of implicit associations between document content as an alternative to subtopic mining. We also explore subtopic mining from document anchor text and anchor links. In addition, we investigate the suitability of inter-document similarity as a measure for diversity-aware retrieval models, with the aim of using measured inter-document similarity as a replacement for diversity-aware evaluation models that rely on subtopic mining. Finally, we investigate the suitability and application of document similarity for requirements traceability. We present a fast algorithm that uncovers associations between various versions of frequently edited documents, even in the face of substantial changes.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references (leaves 220-227). / Microfiche. / xiii, 227 leaves, bound ill. (some col.) 29 cm

Most information retrieval (IR) systems respond to users' representation of their information needs (queries) with a ranked list of relevant results, usually text documents. XML documents di er from traditional text documents by explicitly separating structure and content. XML-IR systems aim to exploit this separation by searching and retrieving relevant components of documents (called elements) rather than entire documents thereby, better ful lling users' information needs. Despite the potential bene t of XML-IR systems, most research in this area has not been centered on the needs of users. In particular, current XML-IR query formation interfaces, namely keywords-only and formal language, are not able to optimally address the needs of users. Keywords-only interfaces are too unsophisticated to fully capture the users' complex information needs that contain both content and structural requirements. In contrast, while formal languages are able to capture users' content and structural requirements they are too di cult to use, even for experts, and are too closely tied to the physical structure of the collection. This thesis presents a solution to these problems by presenting NLPX, a natural language interface for XML-IR systems. NLPX allows users to enter XML-IR queries in natural language and translates them into a formal language (NEXI) to be processed by existing XML retrieval systems. When evaluated by system testing, NLPX outperformed alternative translation approaches. When tested in a user-based experiment, NLPX performed comparably to a query-by-template interface, the baseline user-oriented interface for formulating structured queries. It is hoped that the outcomes of this thesis will help to refocus the eld of XML-IR around the user. This will lead to the development of more useful XML-IR systems, which will hopefully result in the more widespread use of XML-IR systems.

This dissertation presents a study of creating maps that can be used to help people seek information from Internet documents. The study involves several different research areas in computer and information science including web mining, data mining, artificial neural network in particular self organising maps (SOM), information visualisation, user interface and information retrieval. The purpose of this dissertation is to offer an alternative way to retrieve information by visually representing the characteristics of the unseen documents and their relationships on the 2-dimensional surface of the SOM. The process starts with collecting documents that include text and images from the Internet, moving to extracting important features from them. In other words, we are performing an information retrieval indexing process. The document features are then clustered by using the SOM. As a result, documents with similar features will be clustered together on 2-dimensional maps. The maps are labelled and the documents are connected to locations on the maps based on the labels. The maps are then arranged hierarchically and visualised so that they can be used as a browsing and exploration tool for information retrieval. / We propose a novel method to automatically label the SOM, called HLabelSOM, that produces hierarchical maps and allows documents to place more than one location on the map. In a visualisation interface, called DocMap, we display these hierarchical maps to help people seeking information. The different levels of the hierarchical maps are able to serve users with different information needs, form the needs of general information to the needs of documents in specific topics. Moreover, users may change their intent in the search process, switching from a more general to a more detailed focus or vice versa. The flexibility of placing documents in more than one location itself increases the chance to find the desired documents. Most importantly, by using DocMap a mental contact between a user and the set of documents is established. The user is able to see the relationships among documents topics and find the desired documents with reasonable time and effort. / Thesis (PhD)--University of South Australia, 2005.

In this thesis, we address several fundamental concerns of maintaining and querying huge ordered label trees. We focus on practical implementation issues of storing, updating and query optimization of XML database management system. Specifically, we address the XML order maintenance problem, efficient evaluation of structural join, intrinsic skew handling of join, succinct storage of XML data and update synchronization of mobile XML data.

Thesis (M.S.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 21, 2008) Includes bibliographical references.

Zugl.: Aachen, Techn. Hochsch., Diss., 2007

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 81-85).