The SIMILIS project endeavors to craft an intelligent semantic tool, specifically designed to enrich user interaction with the ubiquitous ‘Help & FAQs’ section on the websites of IP management entities, such as the EUIPO. A pivotal objective is the detection of whether user-submitted queries have already been addressed in existing FAQs, thereby averting redundant responses from staff. SIMILIS is intricately tailored to identify duplicate or equivalent questions by harnessing query semantics, word relationships, and contextual understanding. This approach facilitates a profound understanding of user information needs, leading to a more refined presentation of search results.
To realize these objectives, SIMILIS will capitalize on Natural Language Processing (NLP) techniques and implement supervised learning through Deep Learning models. This strategic use of technology aims to elevate the user experience by discerning the relevance of retrieved results within the specific context of each query. In practical terms, SIMILIS empowers users to receive quicker and more precise responses, fostering satisfaction. Simultaneously, it enables staff to save time, redirecting their focus toward addressing new or complex queries.