Pragmatics Aware Querying in Heterogeneous Knowledge Graphs

With the increase in structured Linked Data sources, Knowledge Graphs(KG) are becoming indispensable in areas like Question Answering, Web Search and Data Analytics. Given the richness present in the KG schema definitions, more expressive user querying is possible. However, accessing KGs is still challenging, because the rich schema is not exposed in a more intuitive way to the naive user. This results in user intent not being translated into a precise query satisfying all the user needs. My thesis focuses on making KGs accessible for querying by using Gricean notions of Cooperative Answering. More specifically, using Query Reformulations, Data Awareness, and Pragmatic Context, KGs can be made more responsive to user requirements and provide quality results in context.

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