Lessons Learned from a Knowledge-driven Search Application on-top of Large Data Sets

International audienceThe Web stores huge amounts of data. Additionally the number of stored data is increasing permanently. Hence, while using a search application, after some time it is likely that users are confronted with unknown instances or properties while triggering their search questions. From this observation the research challenge is derived, how data can automatically be visualized for a user without leaving the search application. Here, we will present observations from a search-driven application from the field of Question Answering on-top of the Web of Data. This Web application is using Wikidata-a data source derived from Wikipedia-and several other to provide access to general knowledge and specific knowledge from particular domains. Hence, the size of the data set is very large. The problem is how to tackle the sheer amount of available instances and properties (volume), the high variety due to the ambiguity of natural language questions, and the broad field represented by a general-purpose knowledge base. Data (instances and properties) need to be visualized so that it can be explored with respect to different dimensions and allowing different granularity. Additionally, feedback interaction points were required to make the system learn over time and deal with the ambiguity of natural language questions. Concluding, in this paper we will provide an overview of the challenges we have identified and the derived solutions