Relation Robustness Evaluation for the Semantic Associations

The search tools and information retrieval systems on the contemporary Web use keywords, lexical analysis, popularity, and statistical methods to find and prioritise relevant data to a specific query. In recent years, Semantic web has introduced new approaches to specify Web data using machine-interpretable structures. This has led to the establishment of new frameworks for search engines and information systems based on discovering complex and meaningful relationships between information resources. In this paper we discuss a semantic supported information search and retrieval system to answer users’ information queries. The paper focuses on knowledge discovery aspects of the system and in particular analysis of semantic associations. The information resources are multimedia data, which could be retrieved from heterogeneous resources. The main goal is to provide a hypermedia presentation, which narratively conveys relevant information to the queried term. The structure describes the related entities to the queried topic and a ranking mechanism assigns weights to the entities. The assigned weights express the degree of relevancy of each related entity in the presentation structure