Invention Machine recently reported on the shortcomings of traditional search and how deep semantics can help: “Yesterday, we hosted a webcast on the Future of Research and how Question Answering Technologies – and Deep Semantics, in particular – address the needs of the knowledge worker. The response was overwhelming. Hundreds of registrations for the event, a flood of responses to our pre-event survey and dozens of questions throughout the live broadcast.  Clearly, the topic of search – and the failure of traditional search – has struck a chord.”

The article continues, “The problem starts with the staggering fact that the information available to the knowledge worker today is growing at an explosive and exponential rate. Industry analysts, IDC, estimate a 40-60% year-over-year growth of information. Beyond the sheer volume of data, knowing ‘where’ to look becomes increasingly more challenging. Product development information is scattered across the organization – across teams, departments, verticals, sciences, geographies, etc.  Knowledge exists in personal and corporate drives, in email, field reports, customer service records, PLM and PDM systems, and so on. Corporate intranets, shared drives and repositories do little to solve the knowledge retrieval challenge. And, as demonstrated by our survey – accessing valuable external content is an even greater challenge.”

The article points to a possible solution: “Invention Machine Goldfire is a purpose-built innovation intelligence platform that integrates Deep Semantics capabilities with proven innovation tools and methods, collaboration capabilities and access to rich technical content to: (1) Power breakthrough search experiences to deliver precisely relevant concepts. (2) Help innovation workers explore problems and their solutions. (3) Unlock the value hidden deep in internal and external knowledge sources to speed discovery and knowledge-enable decision making. (4) Stimulate idea generation and accelerate inventive problem solving.”

Image: Courtesy Invention Machine

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