Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns and make decisions like humans, but with minimal human intervention.

The problem with likening machine learning to human learning is that when humans learn, they connect the patterns they identify to high order semantic abstractions of the underlying objects and activities. Our background knowledge and perspective give us the necessary context to reason about those patterns and identify the ones most likely to represent robust actionable knowledge.

Semantic enrichment gives data more meaning, making it more easily discoverable by both users and search engines. Semantic search is changing many things, but especially the way digital marketing is or at least should be performed.

Semantic relationships are strengthened by professional annotators who hand-tune the results, and the algorithms that generate them. Web searchers tell the algorithms which connections are the best through what they click on.

By improving core natural language processing technologies for automatic information extraction and classification of texts, search engines built on these technologies can continue to improve.

More sophisticated search algorithms now incorporate semantic search principles when matching the query to an answer and when ranking content. Semantic search is useful to provide deeper meaning to a searcher’s intent by evaluating the entity connections between sentences, words, possible contextual meanings, and the person’s search history.

Graph data stores may easily model, explore and query data that contains complex interrelationships across data entity silos, but the need for specialized skills has limited their adoption to date. Graph analytics are growing in an effort to meet the needs of complex questions asked across explosively complex data.

Empowering searchers and search marketing professionals to access databases using simple keywords may offset the steep learning curve of mastering a structured query language. Semantic search expands search engines and digital marketers’ understanding of complex and possibly fast-evolving data schemas.

Semantic technology and new AI language continue to evolve and be used in a variety of applications. It has never been more important to have someone with the expertise and knowledge handling your content, developing your taxonomies and making your information findable.

Melody K. Smith

Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.