The set of OrganiK KM Client Interfaces comprises of a Wiki, a Blog, a Social Bookmarking and a Search Component that together constitute a Collaborative Workspace for SME knowledge workers. Each of the components consists of a Web-based client interface and a corresponding server engine.
The components that comprise the Business Logic Layer of the OrganiK KM Server are:

  • the Recommender System,
  • the Semantic Text Analyser,
  • the Collaborative Filtering Engine
  • the Full-text Indexer
The Wiki Component is a web-based collaborative authoring tool that allows the knowledge workers of the participating SMEs to simultaneously create, edit, and share the knowledge artefacts of the company (i.e. documents, diagrams, designs, photographs, binary files, etc).

The Blog Component provides a simple content management tool enabling the knowledge workers of the participating SMEs to build easily updatable web project monitoring diaries. Posts are published chronologically and in an open manner, with links and commentary relating to various aspects of each specific project.

The Social Bookmarking Component enables knowledge workers of the participating SMEs to collect and annotate (tag) their favourite resources (e.g. intranet documents, Wiki entries, blog posts, etc) in an online, open environment, which other employees are free to read and use (bookmarks are stored in a central server location and are accessible over the Web from any location).

The Semantic Search Component supports browsing, searching, retrieval and display of knowledge resources. It provides an advanced search functionality based on the available semantics.

The Recommender System is the central provider for ontological reasoning services. In extension to traditional reasoning approaches, the Recommender will also handle the weak semantics of tag clouds and socially-evolving terminology spaces. Faced with these dynamic and vague foundations, the core services provided by the Recommender are a) the suggestion of tags and classifications; b) the suggestion of related information items.

The Semantic Text Analyser processes the text of information items within the system, using linguistic and statistical algorithms. The core result of this analysis is the identification of existing entities such as used tags or entities in existing databases (e.g. customers) which can be used by the Recommender System to suggest the most appropriate tags and classifications. As a number of well-known text analysis systems are available within the scientific community, OrganiK will concentrate on the selection of suitable tools and their integration and mash-up within the system architecture. Based on previous cooperation and a preferential access to detailed information, IBM's UIMA system is a preferred candidate for this.

The Collaborative Filtering Engine enables individual knowledge workers to benefit from the common search experience within their groups. Individual searches - whether traditional keyword-based searches or searches using the enhanced capabilities of the semantic search components - will result in lists of retrieved documents; it remains the user's task to select the most relevant documents from such lists.

To complete the range of content retrieval techniques proposed in OrganiK, a Full Text Indexer will be integrated into the system architecture. This component will enable the efficient retrieval of documents containing queried text snippets/key words. We plan to rely on the well-known Lucene system or similar tools as a basis for this component.