Do it yourself (DIY) Jeopardy QA System (Demo)

The evolution and maturity of semantic technologies techniques and frameworks are bringing functionalities which were once considered academic or prototyp- ical 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 seman- tic 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 data, semantic flexibility and integration between struc- tured and unstructured resources. The success of these projects in demonstrating the potential of existing technologies lies on the fact that they bring into a sin- gle system approaches from Natural Language Processing (NLP), SemanticWeb (SW), Information Retrieval (IR) and Databases. This work demonstrates Treo, a framework which converges elements from NLP, IR, SWand Databases, to create a semantic search engine and question an- swering (QA) system for heterogeneous data. Jeopardy and Question Answering queries over open domain structured and unstructured data are used to demon- strate 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.