SBIR Phase II: Hybrid Question Answering Combining a Search Index with an RDF Store

Period of Performance: 01/01/2012 - 12/31/2012


Phase 2 SBIR

Recipient Firm

Lymba Corp.
1701 N. Collins Blvd., Suite 2200
Richardson, TX 75080
Principal Investigator, Firm POC


This Small Business Innovation Research (SBIR) Phase II project will provide integrated, seamless access to structured and unstructured information. At present there is no easy way to perform a single federated search across structured databases and unstructured text documents, and complex applications over diverse data sources require considerable time and effort to develop. The multilingual Hybrid Question Answering (HQA) engine will change the way people access heterogeneous data by answering complex questions over semantic models from the database and free text from the search indexes of product literature, call center data, social media, etc. HQA will answer a broad range of questions including factoid, procedural, explanation and scenario-matching. Users will also be able to discover facts, relationships among events, and sentiments; to identify trends and enable predictions. Finally, to ensure high precision, HQA will process information in its native form for 11 languages, including English. A minimum score of 70% MRR is expected for the English HQA system and 60% MRR for the other 10 languages. The broader impact/commercial potential of this project spans several key areas. The HQA engine will surpass existing Questioning Answering technology by answering a wide range of complex questions with information found in both structured and unstructured data sources. Social media-based applications such as trend finding, sentiment detection, and predictive analysis operating in international markets will greatly benefit from the availability of the multi-lingual HQA system. Customer Relation Management (CRM), Business Intelligence and Social Media are among the primary commercial applications benefiting from the development of the HQA engine. The impact of this technology will result in businesses being able to develop more personalized relationships with customers as a result of a better understanding of customer needs and motivations. Traditional business relationships will become more responsive, resulting in greater understanding and trust, which will mitigate destructive, wide swings in financial markets caused by poor predictive models.