Let’s take the first item: The searcher uses terms and phrases in their search query that aren’t mentioned in the relevant topical documents. In the past, this problem was tackled by maintaining a thesaurus of similar and relevant terms but keeping a thesaurus up to date can be a time consuming and manual practice. Today, using deep learning language models like BERT as part of the search algorithm lets a search system find similar or more relevant documents without the need for a manual thesaurus.
Make Data Findable
Performing natural language processing (NLP) of documents while indexing them can also be beneficial. Entities such as people, places, events, and other things can be extracted and used as metadata in the documents to help searchers understand what a document may be about. Similarly, relationships between entities can also be extracted using NLP that also helps the search system to recognize what documents are about. Finally, NLP can also be used to categorize and classify documents automatically, making it easier for searchers to differentiate between groups of documents when a search term or phrase returns too many documents to be useful to the searcher.
These AI-generated classifications and categorizations can be helpful to a searcher who is not sure what they’re looking for. By browsing the various categories, searchers can more easily get a feel for what information is indexed in that system relevant to the terms that the searcher used in their queries.
Personalize Search Results
Another use for AI and ML is to provide the searcher with more personalized results based on their past search history, job role and other pertinent information. This data when used by machine learning algorithms can help the search system to focus on what the searcher was interested in in the past or what may be most interesting related to their job role and return results more closely linked to those items.
Finally, AI and ML can be used by the search system to create smarter indexing systems, eliminating duplicate or near duplicate documents and relating documents together, even though they may be from completely different document repositories. Creating smarter indexing systems makes searches easier and more efficient because unnecessary duplicates are filtered out.
The bottom line is that artificial intelligence and machine learning can be a tremendous boon to search systems and many advanced search systems are now integrating these technologies into their solutions and applications.