Exploratory Querying of Extended Knowledge Graphs.

Knowledge graphs (KGs) are important assets for search, analytics, and recommendations. However, querying a KG to explore entities and discover facts is difficult and tedious, even for users with skills in SPARQL. First, users are not familiar with the structure and labels of entities, classes and relations. Second, KGs are bound to be incomplete, as they capture only major facts about entities and their relationships and miss out on many of the more subtle aspects. We demonstrate TriniT, a system that facilitates exploratory querying of large KGs, by addressing these issues of \"vocabulary\" mismatch and KG incompleteness. TriniT supports query relaxation rules that are invoked to allow for relevant answers which are not found otherwise. The incompleteness issue is addressed by extending a KG with additional text-style token triples obtained by running Open IE on Web and text sources. The query language, relaxation methods, and answer ranking are extended appropriately. The demo shows automatic query relaxation and has support for interactively adding user-customized relaxations. In both situations, the demo provides answer explanations and offers additional query suggestions.