The amount of information constantly grows in today’s information society. Web search engines and various platforms providing search functionality are quite sophisticated systems. Especially Google Search is familiar to practically everyone who uses the internet. On top of the basic Google Search, Google has developed optimized search functionalities, at least for images, videos, shopping, books, traveling, finance, and academic publications. All these functionalities are quite easy to use, even for a new user, which generates high expectations for all other search applications and functionalities.
In addition to Google Search and other openly available search applications, search engines are also needed for finding information from organizational systems. It is highly probable that some ready-made solution is not enough for an organizational search. An internal search application must be tailored to the business and the users’ needs. Having optimized search functionalities for information and knowledge management can have a big impact on the overall performance of an organization if the frequency of utilizing those functionalities by the personnel is high.
Modern search engines with semantic features
Many organizations still internally utilize search functionalities, which can only find exact matches with the given search query. In other words, if “ATR Soft” is written in the search box, the search application only finds results that contain the characters of “ATR Soft” in that specific order. It can be very frustrating for the users if minor typos or additional whitespace characters are not tolerated at all when retrieving the search results. Sometimes extensive knowledge about the surrounding system is required for efficient usage of the search application, which certainly should not be the case.
To aid an organization with its internal search application needs, modern search engines, such as Solr and Elasticsearch, can be utilized to offer fast and intuitive search experiences for users. These search engines have built-in capabilities for enabling flexibility for the query string input: For example, the search engine can find relevant results even if there are typos or if the characters or words are in a different order than they are in the retrieved results. With modern technologies, it is relatively effortless to implement a search application that is easy to use, manage and develop.
In many cases, utilizing the basic built-in capabilities of search engines is enough to gain the desired benefits. Additionally, some semantic capabilities can be added to a search application to provide semantic search results. With AI-powered tools, it is possible to add human-like language understanding to the application. For example, a semantic search engine “understands” that “a port” is a place for ships and that “a harbor” is a synonym for it. A semantic search engine is designed to “understand” those facts and to retrieve results that could be related to the query, even if the results do not include any of the query words. Semantic search accepts a wider variety of queries for finding relevant results, making searching even more effortless and intuitive.
In my thesis, I researched the possibilities of implementing semantic search in a case management system. Feel free to read the thesis if you are interested in taking a closer look at the opportunities of semantic search and creating a modern search application.
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