The evolution and maturity of semantic technologies techniques and frameworks are bringing functionalities which were once considered academic or prototypical into real-life applications. Products such as IBM Watson [1] and Siri are examples of applications which are heavily leveraged on state-of-the-art semantic technologies. These systems provide a synthesis of the functionalities which are available for general applications today such as: natural language search and queries over large-scale

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... ata, semantic flexibility and integration between structured and unstructured resources. The success of these projects in demonstrating the potential of existing technologies lies on the fact that they bring into a single system approaches from Natural Language Processing (NLP), Semantic Web (SW), Information Retrieval (IR) and Databases. This work demonstrates Treo, a framework which converges elements from NLP, IR, SW and Databases, to create a semantic search engine and question answering (QA) system for heterogeneous data. Jeopardy and Question Answering queries over open domain structured and unstructured data are used to demonstrate the approach. In this work, Treo is extended to cope with unstructured text in addition to structured data. The setup of the framework is done in 3 steps and can be adapted to other datasets in a simple DIY process.