QAnswer: Towards Question Answering Search over Websites
This work addresses the gap in QA adoption for website search practitioners, though it is incremental as it builds on existing QA methods.
The paper tackles the problem of integrating question answering (QA) technologies into website search by demonstrating a combined approach using knowledge graphs and free text, applied to Wikimedia websites like Wikipedia and Wikidata.
Question Answering (QA) is increasingly used by search engines to provide results to their end-users, yet very few websites currently use QA technologies for their search functionality. To illustrate the potential of QA technologies for the website search practitioner, we demonstrate web searches that combine QA over knowledge graphs and QA over free text -- each being usually tackled separately. We also discuss the different benefits and drawbacks of both approaches for web site searches. We use the case studies made of websites hosted by the Wikimedia Foundation (namely Wikipedia and Wikidata). Differently from a search engine (e.g. Google, Bing, etc), the data are indexed integrally, i.e. we do not index only a subset, and they are indexed exclusively, i.e. we index only data available on the corresponding website.