Knowledge Base Inference for Regular Expression Queries

Vaibhav Adlakha, Parth Shah, Srikanta Bedathur, Mausam

Two common types of tasks on Knowledge Bases have been studied -- single link prediction (Knowledge Base Completion) and path query answering. However, our analysis of user queries on a real-world knowledge base reveals that a significant fraction of queries specify paths using regular expressions(regex). Such regex queries cannot be handled by any of the existing link prediction or path query answering models. In response, we present Regex Query Answering, the novel task of answering regex queries on incomplete KBs. We contribute two datasets for the task, including one where test queries are harvested from actual user querylogs. We train baseline neural models for our new task and propose novel ways to handle disjunction and Kleene plus regex operators.

Knowledge Graph

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