Many think of AI as human-like robots or human-sounding voices that interact with actual humans, but the real value of artificial intelligence may derive from what it’s doing behind the scenes.
For example, when unsupervised machine learning (a species of AI) is applied to enterprise content, it can develop a deeper understanding of that content and deliver insights to humans in instantly valuable ways.
Content is about more than mere words; it’s about how words interrelate. By analyzing the statistical co-occurrence of terms across enterprise content from a range of sources, machine learning forms sophisticated models that can take search much further. It can identify concepts, extract phrases, suggest better queries, and pinpoint internal SMEs with relevant expertise—whether or not their written profiles even reflect it.
AI-infused search helps people find what they’re looking for, even when they’re not exactly sure what that is. This is what OpenText™ Decisiv is all about.
“Asia Not Asia”
Imagine that you’re looking to understand more about your business operations in Asia. Your first instinct might be to search your various data stores for documents containing “Asia,” but will that term necessarily appear in the most valuable content? For that matter, how many documents will list city, state, country, and/or continent? Without machine learning, you would need an exhaustive search taxonomy to address all these variations.
When a Decisiv user types “asia,” the system instantly and automatically retrieves documents that (1) contain the word Asia and/or (2) are conceptually related to Asia. This not only casts a wider net, it provides results based on a more sophisticated relevancy analysis than simple keyword searching could hope to deliver.
Typing in the seemingly illogical query “asia not asia” is illustrative; it shows the user content that is conceptually related to Asia, but doesn’t contain actually contain the term Asia—displaying only the documents found by unsupervised machine learning above and beyond keyword search.
Concept grouping categorizes documents according to linguistic patterns that we humans have a hard time identifying across large volumes of data. Machine learning makes such conceptual analysis automatic and highly scalable, and humans get the benefit.
Concept groups can also propel a researcher to new areas of useful content. By looking at a list of the key concept groups that appear in response to a search or metadata filter (along with the document counts for those groups) researchers can see an overview of the content that’s available, spot pertinent aliases, adjust their search terms, and include or exclude concepts to meet their objectives.
Concept groups are displayed with characteristic labels (top words and phrases that appear in those groups) to easily provide a sense of what each group represents, and how useful it’s likely to be for a given search.
Take Decisiv Action
Register for OpenText’s annual user conference, Enterprise World, in Toronto this July to learn how AI-enhanced search can help empower your digital transformation. You’ll hear from top users of Decisiv Search and our other award-winning Discovery products how they’re leveraging machine learning for more effective enterprise information management.
All humans welcome.