Many things have changed since 2010 when search engine optimization (SEO) was more concerned with getting as many backlinks as you could and including as many keywords as possible. The focus has shifted to understanding intent and behavior, and the context – semantics – behind them.
Machine learning or semantic technology is the basis for most commercial artificial intelligence (AI) systems. The possibilities and limitations of semantic search grow as more and more enterprises and applications utilize the technology. Research on the subject has grown almost as fast as the technology itself. Semantic search research is often based on large and rich data sets and a combination of techniques.
The development of AI and natural language processing (NLP) technologies have revolutionized the way a search engine retrieves information. Semantic search seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace. This is true in a closed system or on the Internet. It is providing context to a search query beyond just keywords.
This is different from pure keyword search, and it can have a strong impact. A pure keyword search works roughly by matching query words to words in documents. Conversely, semantic search will often return results where there are no word matches, even with NLP applied, but the content still plainly matches what the user seeks.
Semantic search can do this because semantic search engines work differently. They don’t match on text, but on meaning. By looking at a massive dataset, it doesn’t come up with a formal definition of words, but it does understand what the words mean based on its context or usage, and what other words can be used in the same or similar contexts.
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 aims to get at the real intent of the query, rather than simply matching a page to a search string.
Data Harmony is a fully customizable suite of software products designed to maximize precise, efficient information management and retrieval. Our suite includes tools for taxonomy and thesauri construction, machine aided indexing, database management, information retrieval and explainable AI.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.