Semantic AI: The Benefits and Impact of Machine Learning with Tridion
By Saurabh Gangwar
A decade ago, the concepts of machine learning (ML) and artificial intelligence (AI) may have appeared theoretical and complex, with few practical use cases. Fast forward to today, and we’ve witnessed the remarkable growth in the popularity of ML and AI, driven by their diverse and valuable applications.
When integrated with a content management system (CMS) like Tridion, semantic AI can help to unlock new capabilities that provide an improved digital experience.
Businesses often find themselves in situations where they have to navigate siloed, unorganized, and duplicate information as they try to generate insights that help to form new ideas. Machine learning and semantic AI are valuable assets in this context, particularly in the realms of content management, personalization, and the creation of engaging content experiences.
According to McKinsey, global AI adoption is 2.5x higher than in 2017, despite leveling off at around 50% over the last few years. Whether through chatbots, virtual assistants, or automation tools, machine learning empowers companies to elevate workforce productivity and efficiency, enabling them to achieve key objectives more rapidly.
In this article, we’ll explore the concept of semantic AI, its benefits, and its practical implementation in Tridion.
What is Semantic AI?
Semantic AI, which is also related to natural language processing (NLP) or natural language understanding, is a branch of artificial intelligence focusing on how computers understand and process human language. It involves analyzing and interpreting the meaning of words and sentences and using those details to perform tasks or provide information to users.
For example, a semantic AI system can automatically summarize a news article, answer questions, or translate text from one language to another. Semantic AI systems often rely on machine learning algorithms to improve their accuracy and performance over time.
Benefits of Semantic AI
With the inclusion of semantic AI into Tridion, businesses can receive several benefits.
Improved Data Quality
Semantic AI can enhance the quality of data available by improving predictions and classification, making data more organized and streamlined.
Improves Search and Findability
A more robust taxonomy makes finding the right content assets easier for both content authors and the end consumer. Also, by understanding search intent and with the help of faceted search, the most relevant results can be displayed to consumers, reducing the time they need to spend wading through excess information.
Content editors and authors can quickly and accurately tag content without having to do so manually. Instead of organizing and classifying content, they can focus on creating content that drives desired results.
Semantic AI can provide better insights to marketers and other content creators as it can be used to extract relevant information from unstructured data. Semantic AI can also offer improved semantic analysis to draw conclusions from customer feedback faster than if they tried to do so manually.
Better Customer Experience
Semantic AI can be used to implement chatbots that draw on answers from a knowledge database and existing content assets. These can answer customer queries much faster than in-person support and provide an opportunity for more personalization, thus delivering on the promise of a better customer experience.
How Tridion Uses Semantic AI
Semantic AI connects Tridion to the knowledge databases throughout an organization, integrating content with data to provide all users with the information they require. Here are some of the features that highlight how Tridion leverages the power of semantic AI:
Semantic AI adds context and matches user intent to content using machine learning. When a customer searches for a term, semantic AI makes relevant suggestions and recommendations. It also allows users to apply faceted search to narrow down search results.
For example, if a customer was browsing an insurance website to find out how to file a claim, they could enter “file a claim” into the search field and then use faceted search to narrow down the results for auto insurance and receive only the most relevant results.
While many CMSs offer siloed and limited taxonomy features. Tridion uses Taxonomy Spaces to improve data sharing and governance with the help of Semantic AI. It manages all important concepts within an organization, the various ways to describe them, and how they relate to one another. For example, under the insurance category, you might find “co-insurance” and “insurance company.” There might also be related terms such as “insurance law.”
Content authors must browse extensive taxonomies to add the correct terms to content. Tridion offers automated tagging suggestions that provide relevant tags while editing content. For example, a blog article about Best Insurance Companies in the Southern US could see labels generated for insurance, insurance claims, Florida, Georgia, and more. Smart Tagging saves content authors time, and tags can be accepted or updated as necessary.
Leverage Semantic AI and Tridion
Machine learning algorithms and artificial intelligence can be valuable tools to help increase employee productivity and deliver a better customer experience. With the latest Semantic AI capabilities, Tridion users can get even more out of AI with features like Conceptual Search, Taxonomy Spaces, and Smart Tagging.
However, these latest features only scratch the surface of what Tridion can provide for managing structured content. At Content Bloom, we have deep expertise in the various Tridion capabilities and can help you get the most out of the CCMS and semantic AI. We can help you to avoid content silos and gain control of the content lifecycle with improved findability and reusability.
Learn more about structured content and the benefits of having a solid structured content strategy by reading: Best Practices for Implementing a Structured Content Strategy.
What is Machine Learning?
Machine learning is a field of artificial intelligence that focuses on using and developing computers that can imitate how humans learn. Essentially, it can interpret and adapt as it gathers more information. For example, Netflix’s recommendation engine provides more accurate recommendations after it begins understanding your viewing preferences.
What is Machine Learning vs. AI?
Machine learning is a subset of artificial intelligence. AI encompasses a wide range of techniques and technologies for creating intelligent systems, while machine learning is a specific method used within AI to enable machines to learn from data and make predictions or decisions.
What are semantics in Artificial Intelligence and Machine Learning?
Semantics is the historical study of meaning. In artificial intelligence and machine learning, semantics refers to the interpretation of language or data by computers.
What does Tridion Semantic AI do?
Tridion semantic AI matches user intent and context to content to improve search performance, automatically tags relevant content assets, and enhances the organization and classification of content assets throughout the business.
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