Why Not All Semantic Search Engines Are the Same
Patent filing statistics and research paper publications are on a seemingly unstoppable upward trend worldwide. All of this innovative activity also means that there is an unprecedented abundance of information available. IP professionals now require a solution that can sift through millions of complex documents to find relevant prior art and uncover powerful insights.
Technology is changing daily and is becoming increasingly interdisciplinary. Having a system that learns new conceptual relationships from ingested content and imposes no bias onto how topics should interact with each other can lead to discoveries between topics and previously hidden connections that can ultimately lead to opportunities.
The Data Explosion Continues
• More data has been created in the past 2 years than in the history of the human race.
• Global scientific data is doubling every 9 years.
• Global patent applications reached 2.9 million, a 7.8% increase.
• Global trademark applications jumped 15.3% to 6 million.
|Industrial design applications||853,500||872,800||2.3|
Is your search engine keeping pace?
Some solutions use descriptors like, ‘semantic search, ‘natural language searching’ or ‘intelligent search’ to refer to systems that return a knowledge representation based on entity and fact extraction. These systems rely on ontologies to define search terms-in other words, they do not represent a huge leap forward from older search engines that use pre-defined datasets and logical operators in order to come up with their results.
While adding some value beyond traditional keyword retrieval, they typically require structured text that has been manually annotated with a machine-readable, industry-standard mark-up language. These taxonomies need to be manually updated as technology evolves and can create technology silos based on preconceived notions about how topics should be categorized. Find the hidden insights you may be missing out on:
• Insights and opportunities that span the lifecycle of innovation.
• Critical prior art from non-patent literature.
• Business threats that are discoverable through semantic mapping.
Hybrid Search is the Future
The latest technologies overcome the limitations of the past by integrating AI-driven search with traditional Boolean. This improves efficiency and thoroughness to deliver quality results, faster. But it takes some powerful capabilities to achieve unsupervised, deep learning neural networks; dual models of Semantic search plus keyword; and key concept learning by creating semantic signatures.
Applying these capabilities to patent and scientific information transforms complex IP data into a simple and intuitive form that you can act on.
Download our Find Insights at Every Stage of the Innovation Cycle with Artificial Intelligence infographic to learn more about the benefits of semantic search and how it improves the innovation process.
Source: 1790 Analytics LLC. Analysis of US Patent Referencing to IEEE Papers, Conferences, and Standards 1997-2017.