A Machine Learning Algorithm for Searching Vectorised RDF Data
Asaad Sabah Hadi, Paul Fergus, Chelsea Dobbins, Abbas M. Al-Bakry · 2013
The Internet has fundamentally changed the way we collect, access, and deliver information. However, this now means that finding the exact information we need is a significant problem. While search engines can find information based on the keywords we provide, using this technique alone is insufficient for rich information retrieval. Consequently, solutions, which lack the understanding of the syntax and semantics of content, find it difficult to accurately access the information we need. New approaches have been proposed that try to overcome this limitation by utilising Semantic Web and Linked Data techniques. Content is serialised using RDF, and queries executed using SPARQL. This approach requires an exact match between the query structure and the RDF content. While this is an improvement to keyword-based search, there is no support for probabilistic reasoning to show how close a query is to the content being searched. In this paper, we address this limitation by converting RDF content into a matrix of features and treat queries as a classification problem. We have successfully developed a working prototype system to demonstrate the applicability of our approach.