The UMLS Knowledge Source Server: an experience in Web 2.0 technologies.

The UMLS Knowledge Source Server (UMLSKS), developed at the National Library of Medicine (NLM), makes the knowledge sources of the Unified Medical Language System (UMLS) available to the research community over the Internet. In 2003, the UMLSKS was redesigned utilizing state-of-the-art technologies available at that time. That design offered a significant improvement over the prior version but presented a set of technology-dependent issues that limited its functionality and usability. Four areas of desired improvement were identified: software interfaces, web interface content, system maintenance/deployment, and user authentication. By employing next generation web technologies, newer authentication paradigms and further refinements in modular design methods, these areas could be addressed and corrected to meet the ever increasing needs of UMLSKS developers. In this paper we detail the issues present with the existing system and describe the new system's design using new technologies considered entrants in the Web 2.0 development era.

UMLSKS SUGGEST: an auto-complete feature for the UMLSKS interface using AJAX.

The idea behind any auto-complete feature is to suggest words or phrases which are likely to complete what the user is typing. The user can then select a suggestion and avoid typing it in full. This feature by all accounts has enhanced the usability of web interfaces. Typically browsers implement this feature by caching a fixed number of queries, previously entered by the user on the client side. Google Suggest (http://ajaxpatterns.org/Suggestion) is an application that replaces a browser's auto-complete feature with one specific to Google searching. Implementing a google suggest like feature requires a large server infrastructure, as basically every time you type a letter, a database is being hit. One of the ways that this feature can be implemented by many applications without access to a large server infrastructure would be to create a small list of suggestions that contains the most likely suggestions for any given application In this poster we describe the methodology used to create a list of suggestions for the UMLS Knowledge Source Server (UMLSKS) interface. We believe that this methodology can be used by any web based application to create a list of suggestions.

Plug-and-play UMLS knowledge source server using web services and portlets.

The UMLS is large and complex and presents significant challenges in retrieving information in a comprehensive way. Typical users of the UMLSKS (UMLS Knowledge Source Server) run the gamut in terms of knowledge of the UMLS and retrieval needs from the UMLSKS. Even though we as developers of the UMLSKS have always strived to create a truly modular and flexible system, we felt that the available technology until recently was not mature enough to create a loosely coupled, plug-and-play UMLSKS. In the past year we became convinced that web services and portal technology had sufficiently matured to allow us to develop such a system. In this poster, we present the details of the new plug-and-play UMLSKS that has been in development for about a year.

The UMLS knowledge source server: an object model for delivering UMLS data.

The Unified Medical Language System (UMLS), a project of the National Library of Medicine (NLM), regularly distributes a set of knowledge sources to the research community. These data are made available over the Internet through the UMLS Knowledge Source Server (UMLSKS). The new version of the UMLSKS is a complete redesign of the original system using Java and the Extensible Markup Language (XML) technologies to implement a fast, reliable, flexible, and extensible UMLS data retrieval system that includes an Application Programmer's Interface (API) and an Object Model of each of the Knowledge Sources: the UMLS Metathesaurus, the Semantic Network, and the SPECIALIST Lexicon. In this paper we present the design of the new system, outline each of the system design goals, the UMLS Object Model, and statistics showing the usage of the new UMLSKS and associated data. We conclude with implications for future work.