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Why Question-Answering Engines?
Question-answering (QA) systems respond to questions that someone can ask in natural language. This technology is already widely adopted and now rapidly gaining importance in the business environment, where the most obvious added value of a conversational AI platform is improving the customer experience. Another key tangible benefit is the increased operational efficiency gained by reducing call center costs and increasing sales transactions. More recently we have seen a strong developing interest in in-house use cases, e.g., for IT service desk and HR functions.
What if you didn’t have to painstakingly sift through your spreadsheets and documents to extract the relevant facts, but instead could just enter your questions into your trusty search field? This is optimal from the user’s point of view, but transforming business data into knowledge is not trivial.
It is a matter of linking and making all the relevant data available in such a way that all employees—not just experts—can quickly find the answers they urgently need within whichever business processes they find themselves. With the power of knowledge graphs at one’s disposal, enterprise data can be efficiently prepared in such a way that it can be mapped to natural language questions. That might sound like magic, but it’s not.