Mining semantic relations between research areas

For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the finegrained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard

Scatter matters: Regularities and implications for the scatter of healthcare information on the Web

Despite the development of huge healthcare Web sites and powerful search engines, many searchers end their searches prematurely with incomplete information. Recent studies suggest that users often retrieve incomplete information because of the complex scatter of relevant facts about a topic across Web pages. However, little is understood about regularities underlying such information scatter. To probe regularities within the scatter of facts across Web pages, this article presents the results of two analyses: (a) a cluster analysis of Web pages that reveals the existence of three page clusters that vary in information density and (b) a content analysis that suggests the role each of the above-mentioned page clusters play in providing comprehensive information. These results provide implications for the design of Web sites, search tools, and training to help users find comprehensive information about a topic and for a hypothesis describing the underlying mechanisms causing the scatter. We conclude by briefly discussing how the analysis of information scatter, at the granularity of facts, complements existing theories of information-seeking behavior.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69202/1/21217_ftp.pd

Bringing order to the Web: Automatically categorizing search results.

ABSTRACT We developed a user interface that organizes Web search results into hierarchical categories. Text classification algorithms were used to automatically classify arbitrary search results into an existing category structure on-thefly. A user study compared our new category interface with the typical ranked list interface of search results. The study showed that the category interface is superior both in objective and subjective measures. Subjects liked the category interface much better than the list interface, and they were 50% faster at finding information that was organized into categories. Organizing search results allows users to focus on items in categories of interest rather than having to browse through all the results sequentially

EPOS : evolving personal to organizational knowledge spaces

EPOS will leverage the user´s personal workspace with its manyfold native information structures to his personal knowledge space and in cooperation with other personal workspaces contribute to the organizational knowledge space which is represented in the organizational memory. This first milestone presents results from the project´s first year in the areas of the personal informational model, user observation for context elicitation, collaborative information retrieval and information visualization

Strategy hubs: Domain portals to help find comprehensive information

Recent studies suggest that the wide variability in type, detail, and reliability of online information motivate expert searchers to develop procedural search knowledge . In contrast to prior research that has focused on finding relevant sources, procedural search knowledge focuses on how to order multiple relevant sources with the goal of retrieving comprehensive information. Because such procedural search knowledge is neither spontaneously inferred from the results of search engines, nor from the categories provided by domain-specific portals, the lack of such knowledge leads most novice searchers to retrieve incomplete information. In domains like healthcare, such incomplete information can lead to dangerous consequences. To address the above problem, a new kind of domain portal called a Strategy Hub was developed and tested. Strategy Hubs provide critical search procedures and associated high-quality links to enable users to find comprehensive and accurate information. We begin by describing how we collaborated with physicians to systematically identify generalizable search procedures to find comprehensive information about a disease, and how these search procedures were made available through the Strategy Hub. A controlled experiment suggests that this approach can improve the ability of novice searchers in finding comprehensive and accurate information, when compared to general-purpose search engines and domain-specific portals. We conclude with insights on how to refine and automate the Strategy Hub design, with the ultimate goal of helping users find more comprehensive information when searching in unfamiliar domains.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49287/1/20238_ftp.pd

EPOS : evolving personal to organizational knowledge spaces

EPOS will leverage the user´s personal workspace with its manyfold native information structures to his personal knowledge space and in cooperation with other personal workspaces contribute to the organizational knowledge space which is represented in the organizational memory. This first milestone presents results from the project´s first year in the areas of the personal informational model, user observation for context elicitation, collaborative information retrieval and information visualization

Os Catálogos de Nova Geração na descoberta da informação: Os OPAC’s das Bibliotecas do Ensino Superior em Portugal

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Ciências da Informação e Documentação, Área de Especialização em BiblioteconomiaO modo como a informação é produzida e acedida na internet, atualmente, influencia profundamente a forma como os indivíduos interagem com os sistemas de procura da informação. Sítios Web como a Google e Amazon produziram mudanças significativas no ato de pesquisa e acesso a informação, proporcionando ferramentas simples e eficazes nos serviços que disponibilizam. O desenvolvimento deste tipo de interfaces evidenciou a necessidade de acompanhamento das bibliotecas universitárias face aos comportamentos dos utilizadores. Este estudo teve como principal objetivo analisar o nível de desenvolvimento dos catálogos das bibliotecas universitárias portuguesas e a forma como se aproximam do conceito de Catálogo de Nova Geração. Os dados observados revelam um significativo distanciamento ao modelo pretendido, evidenciando um desencontro entre o serviço de pesquisa disponibilizado e as expectativas de um utilizador universitário. Verificou-se maior aceitação das componentes de nova geração nas universidades públicas em relação às privadas e institutos politécnicos. Ao compararmos os resultados obtidos com estudos estrangeiros é possível afirmar que existe um atraso generalizado no desenvolvimento dos catálogos e que as bibliotecas universitárias portuguesas não estão distantes das suas congéneres internacionais, quanto a implementação de um Catálogo de Nova Geração

Applying Wikipedia to Interactive Information Retrieval

There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval