A Semantic Search Approach to Task-Completion Engines
Darío Garigliotti · 2018
Web search is a human experience of a very rapid and impactful evolution. It has become a key technology on which people rely daily for getting information about almost everything. This evolution of the search experience has also shaped the expectations of people about it. The user sees the search engine as a wise interpreter capable of understanding the intent and meaning behind a search query, realizing her current context, and responding to it directly and appropriately[1]. Search by meaning, or semantic search, rather than just literal matches, became possible by a large portion of IR research devoted to study semantically more meaningful representations of the information need expressed by the user query. Major commercial search engines have indeed responded to user expectations, capitalizing on query semantics, or query understanding. They introduced features which not only provide information directly but also engage the user to stay interacting in the search engine result page (SERP). Direct displays (weather, flight offers, exchange rates, etc.), rich vertical content (images, videos, news, etc.), and knowledge panels, are examples of this recent evolution trend into answer engines [10].