Scalable Interactive Keyword Query Interface over Knowledge Graph

Abstract

goes here. Existing keyword query systems over knowledge graph can produce interesting results and are easy to use. However, they cannot handle the ambiguities that have matches in the knowledge graph, namely, multiple interpretations may be correct so that they cannot determine which interpretation is what the user expects. And they cannot scale to handle the knowledge graphs with more than billions of triples or thousands of types/predicates. On the one hand, we construct an interactive interface in which the above ambiguities will resort to the user. To enhance the user experience, we formalize the interaction problem and then propose an algorithm to find a best scheme of interaction (i.e., a verifying sequence with lowest interaction times and candidates) based on the dependency relations between mappings. On the other hand, we propose a new schema graph, i.e., type-predicate graph, which has good scalability while containing complete information for building query graph. No matter how large the knowledge graph is, the size of type-predicate graph is always very small because its size depends on the number of types and predicates whose number are far less than that of triples in knowledge graph. Finally, we have demonstrated our contributions with several well-directed experiments over real datasets (DBpedia and Yago).