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  • September 12, 2019

Oracle Launches Smart Content: A suite of AI tools to Improve Content Discoverability and Authoring in Oracle Content and Experience

Guest Author
This is a syndicated post, view the original post here

Authored By: Sree Kamireddy

With the introduction of the Intelligent Content Platform, today we are excited to launch Smart Content for Oracle Content and Experience [OCE]. Smart Content represents a powerful rethinking in content discoverability for Creators & Authors in OCE.

Our customers have thousands of digital assets in OCE, however, these assets are valuable only when they can be discovered at the right time for both the content authors as well as content consumers.

The foundation of content discovery surrounds tagging and classifying content:

  • Tagging Content with Meaning: Decorate the content with metadata characterizing its make-up.
  • Classify the content: Organize the tagged content so that it can be found and delivered in the right context to the users at the right time.

Today, tagging and classification is a painstakingly manual process, which requires a lot of time for initial tagging, classification and for maintaining the accuracy as content evolves. Our modeling shows that a median enterprise needs to spend hundreds of hours monthly to properly maintain this content.  Because of this effort, enterprises are failing to properly tag and classify content, leading to content discovery problems.

In order to alleviate this industry issue, we had to ask 2 questions:

  • During a search, is it possible to find the right assets without the need for manual tagging and classification?
  • During content authoring, can we take it a step further and recommend the right assets without even needing to search?

Smart Content is our answer to the above questions. With Smart Content we apply Artificial Intelligence (AI) to analyze content to augment content discovery. In this release, we are launching 2 primary features, Smart Search and Smart Authoring:

  • Smart Search analyzes and returns relevant images without the need for manual tagging. This improves the discoverability of image assets without having to manually tag images.
  • Smart Authoring recommends the right images during the authoring process. Customers don't need to tag or search for images, they will be recommended images based on the intent expressed in the current article. 

The backbone of Smart Content primarily consists of 2 types of AI models:

  • Computer Vision models that are trained on millions of images to detect the right objects and parameters in new images. As new images are uploaded into repositories, these are Smart Content tagged for the purposes of improving discoverability upon a search.
    • Step 1: Image is auto tagged describing the asset
    • Step 2: Upon conducting a search, relevant results are returned based on the above Smart Content Tags
    • Step 3:  Apart from relevant results, we also provide keyword recommendations in the form of “Smart Keywords” to further help you refine your search. See below picture.

*Tip: In the event your Search is not efficient, you can always manually tag specific assets to improve results for this particular Search.

 

  • Natural Language processing [NLP] models that can derive main intent from an article. The derived intent is used to recommend the right images for the content during authoring process.

 

 

“Smart content facilitates more than simple productivity gains and time savings. The combination of intelligent tagging combined with contextual analysis empowers discovery of imagery and assets authors may not have even known about. Smart content generates better ROI for previously created assets and our content investments.”

-Jason Stortz, Principal at Redstone Content Solutions, LLC

 

For additional details take a look at our short video on “How to use Smart Content” and see our Help Document around Smart Content.

What’s Next?

The launch of Smart Content vastly enhances content discoverability and authoring for all our customers. We understand that each of our customers have unique content discoverability needs and a general platform won’t be sufficient. We are working towards enabling customers to quickly train the above Computer Vision and NLP models to fit their own needs. For example, instead of just identifying a “tire” as a “tire”, custom models will enable customers to train the models with their own datasets allowing the models to identify the particular type of “tire”.

The OCE team is also working on Visual Search features to help users to find the perfect image for authoring purposes that span beyond text. Finally, we are also developing “Smart classification” to help classify content into taxonomies. The classified content not only helps with organization for authors but also helps deliver relevant content to consumers through personalization.

We are excited about the future of Smart Content as Oracle Content and Experience continues to rethink the way our customers can seamlessly manage content and experience channels.

Lots more to come, Stay tuned!

 

Attending Oracle OpenWorld? Don’t miss Sree Kamireddy’s session: The Future of Personalized, Immersive Experiences on Monday, September 16 to learn more and get a live look at how this can change the way your team manages content.

Don’t miss a minute of excitement around Oracle Content and Experience at OOW19 by bookmarking the OCE OpenWorld Site.

Click here to learn more about Oracle Content and Experience!

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