Connecting Intelligence through Federated Knowledge
Semantic platforms currently available in the market consist of proprietary systems that often result in long software development schedules and developing programs from scratch. Time and effort taken to develop programs should be minimal. Mi-Semantic is a software platform for developers to develop semantic applications. All components made using this platform are modular, web service-enabled and SOA (Service Oriented Architecture) compliant. This enables a high level of scalability and performance and thus is able to solve complex problems via interaction between the semantic components and knowledge bases.

Mi-Semantic features are:

• Reduced Development Effort – Structured with platform components, client applications and support tools, developers can intuitively create semantic solutions.
• Reliable and Scalable – Mi-Semantic provides an infrastructure that is flexible and supports semantic feature add-ons and its semantic components produce expected test results every time.
• Defined by world IT standards – Designed according to SOA and W3C standards, Mi-Semantic makes interoperability of the final web solution integratable with other semantic solutions and the Linked-Open-Data Semantic Cloud.

Applications
With the SOA framework, Mi-Semantic can support the development of various kinds of intelligent applications that interface via the W3C web service standard. Some of the many applications of Mi-Semantic are:

• Healthcare/Medical/Life Science Management – Medical records of patients, disease databases and new medical discoveries can be structurally organised for future data entry, search and archiving.
• Education Management – Vertical applications to track student learning performance can be built using the Semantic Assessment Engine and the Semantic Profiling Engine from Mi-Semantic (see Mi-iLMS).
• Risk Management – Growing finance sectors such as Islamic Banking, can utilise Mi-Semantic in their risk portfolio system for better informed decisions based on linked knowledge bases. Analysis and extracting of valuable hidden information from unstructured web data creates intelligence. Social Network Analytics provides the ability to navigate, analyse and visualise the social network. Also applicable are graph manipulation operations, filtering mechanisms and visualisation techniques.
• Intelligent Manufacturing – In electronics manufacturing, data such as yield, failure, reliability and customer return can be in one unified system to help pinpoint root causes and fix issues.
• Enterprise Data, Content and Asset Management – An enterprise can intelligently monitor data and related content across multiple knowledge base sources using various dashboards to facilitate systematic, easier benchmarking and measurement of performance indicators.