MONGOOSE: MONitoring Global Online Opinions via Semantic Extraction

The ever increasing amount of content on the Internet has fostered many efforts seeking to leverage this potentially yottascale information source. Service systems using advanced data and text analytics techniques have been developed to perform knowledge gathering and information discovery over Web data. Information gathered from free and public sources on the Web is frequently integrated with enterprise and proprietary data to create sophisticated service systems able to provide insight in an increasing number of business critical areas. Unfortunately, for fixed and or limited resource projects, consistent and reliable ingestion and integration of content often dominates the effort, reducing the time available for developing core analytics and presentations that differentiate and define an information service. If this initial data extraction, translation and loading of information (known as ETL in the database world) can be abstracted for these web sources, it would provide an important core technology on which Web-based information services could be more rapidly and inexpensively developed and deployed. This paper presents such a system - MONGOOSE - an approach that seeks to reduce the time spent creating a reliable data ingest and integration system and thus reducing the time-to-impact of advanced analytics service solutions.