Semantic technology is a frequent topic of conversation in the information science world. Machine learning or semantic technology is the basis for most commercial artificial intelligence (AI) systems. But where does semantic search fit in?
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.
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 technologies have revolutionized the way a search engine retrieves information.
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.
It goes beyond the ‘static’ dictionary meaning of a query to understand the searcher’s intent within a specific context. By learning from past results and creating links between entities, a search engine can make use of contextual meaning of terms as they appear in the searchable database to generate more relevant results.
With all the changes and new technologies available, 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. Because isn’t that the goal?
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.