Search is changing. It is now more personal, more engaging, more interactive and more predictive. Search results no longer display just 10 blue links — they have become more useful and more visually appealing across all device types. 

Semantic search is at the forefront of these changes.

The word “semantic” refers to the meaning or essence of something. Applied to search, “semantics” essentially relates to the study of words and their logic.

Semantic technologies have been around for more than 40 years. Search engines have always been at the forefront of driving the artificial intelligence (AI) initiative. The application of these technologies make possible the natural language processing (NLP) capabilities of IBM Watson and the ongoing refinement of Google search results, to name a few.

Algorithms can (and do) solve many problems, but having one match the conversational level of human beings presents an enormous challenge.

Understanding how semantic search works at a conceptual level — as well as understanding where it is going — is the key to leveraging it.

Semantic search aids users in locating answers for their search in rapid time. AI can do this by following the purpose of search phrases. It entails discovering the connection between words and phrases in the search inquiry. Machine learning strategy assists search engines in comprehending what information individuals might need based on their search history and also customer identity.

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.

Semantic search brings about an enhanced understanding of searcher intent, the ability to extract answers and delivers more personalized results. Google’s Knowledge Graph is a paradigm of proficiency in semantic search.

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

The future holds more potential and possibilities as semantic technology continues to evolve. With the world’s data doubling every two years, big data has become the norm for players in the online realm. All this data creates an overarching concern of “What does this mean to me?” The process of organizing, structuring and semantically connecting data is a coveted role for search engines.

Data Harmony is Access Innovations’ AI suite of tools that leverage explainable AI for efficient, innovative and precise semantic discovery of new and emerging concepts to help find the information you need when you need it.

Melody K. Smith

Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.