  Knowledge Base Question Answering (KBQA) systems have the goal of answering
complex natural language questions by reasoning over relevant facts retrieved
from Knowledge Bases (KB). One of the major challenges faced by these systems
is their inability to retrieve all relevant facts due to factors such as
incomplete KB and entity/relation linking errors. In this paper, we address
this particular challenge for systems handling a specific category of questions
called temporal questions, where answer derivation involve reasoning over facts
asserting point/intervals of time for various events. We propose a novel
approach where a targeted temporal fact extraction technique is used to assist
KBQA whenever it fails to retrieve temporal facts from the KB. We use
$\lambda$-expressions of the questions to logically represent the component
facts and the reasoning steps needed to derive the answer. This allows us to
spot those facts that failed to get retrieved from the KB and generate textual
queries to extract them from the textual resources in an open-domain question
answering fashion. We evaluated our approach on a benchmark temporal question
answering dataset considering Wikidata and Wikipedia respectively as the KB and
textual resource. Experimental results show a significant $\sim$30\% relative
improvement in answer accuracy, demonstrating the effectiveness of our
approach.
