The ability to retrieve relevant information is at the heart of every aspect of research and development in the life sciences industry. Information is often distributed across multiple systems and recorded in a way that makes it difficult to piece together the complete picture. Differences in data formats, naming schemes and network protocols amongst information sources, both public and private, must be overcome, and user interfaces not only need to be able to tap into these diverse information sources but must also assist users in filtering out extraneous information and highlighting the key relationships hidden within an aggregated set of information. The Semantic Web community has made great strides in proposing solutions to these problems, and many efforts are underway to apply Semantic Web techniques to the problem of information retrieval in the life sciences space. This article gives an overview of the principles underlying a Semantic Web-enabled information retrieval system: creating a unified abstraction for knowledge using the RDF semantic network model; designing semantic lenses that extract contextually relevant subsets of information; and assembling semantic lenses into powerful information displays. Furthermore, concrete examples of how these principles can be applied to life science problems including a scenario involving a drug discovery dashboard prototype called BioDash are provided.

Two important architectural choices underlie the success of the Web: numerous, independently operated servers speak a common protocol, and a single type of client the Web browser provides point-and-click access to the content and services on these decentralized servers. However, because HTML marries content and presentation into a single representation, end users are often stuck with inappropriate choices made by the Web site designer of how to work with and view the content. RDF metadata on the Semantic Web does not have this limitation: users can gain direct access to information and control over how it is presented. This principle forms the basis for our Semantic Web browser an end user application that automatically locates metadata and assembles point-and-click interfaces from a combination of relevant information, ontological specifications, and presentation knowledge, all described in RDF and retrieved dynamically from the Semantic Web. Because data and services are accessed directly through a standalone client and not through a central point of access (e.g., a portal), new content and services can be consumed as soon as they become available. In this way we take advantage of an important sociological force that encourages the production of new Semantic Web content while remaining faithful to the decentralized nature of the Web.

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Computer-based annotation is increasing in popularity as a mechanism for revising documents and sharing comments over the Internet. One reason behind this surge is that viewpoints, summaries, and notes written by others are often helpful to readers. In particular, these types of annotations can help users locate or recall relevant documents. We believe that this model can be applied to the problem of retrieval on the Semantic Web. In this paper, we propose a generalized annotation environment that supports richer forms of description such as natural language. We discuss how RDF can be used to model annotations and the connections between annotations and the documents they describe. Furthermore, we explore the idea of a question answering interface that allows retrieval based both on the text of the annotations and the annotations associated metadata. Finally, we speculate on how these features could be pervasively integrated into an information management environment, making Semantic Web annotation a first class player in terms of document management and retrieval

Dialog boxes that collect parameters for commands often create ephemeral, unnatural interruptions of a program's normal execution flow, encouraging the user to complete the dialog box as quickly as possible in order for the program to process that command. In this paper we examine the idea of turning the act of collecting parameters from a user into a first class object called a user interface continuation. Programs can create user interface continuations by specifying what information is to be collected from the user and supplying a callback (i.e., a continuation) to be notified with the collected information. A partially completed user interface continuation can be saved as a new command, much as currying and partially evaluating a function with a set of parameters produces a new function. Furthermore, user interface continuations, like other continuation-passing paradigms, can be used to allow program execution to continue uninterrupted while the user determines a command's parameters at his or her leisure.