Temporal question answering (QA) is a special category of complex question answering task that requires reasoning over facts asserting time intervals of events.Previous works have predominately relied on Knowledge Base Question Answering (KBQA) for temporal QA.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 (Patidar et al., 2022).A failure to fetch even a single fact will block KBQA from computing the answer.Such cases of KB incompleteness are even more profound in the temporal context.To address this issue, we explore an interesting direction where a targeted temporal fact extraction technique is used to assist KBQA whenever it fails to retrieve temporal facts from the KB.We model the extraction problem as an open-domain question answering task using offthe-shelf language models.This way, we target to extract from textual resources those facts that failed to get retrieved from the KB.Experimental results on two temporal QA benchmarks show promising ~30% & ~10% relative improvements in answer accuracies without any additional training cost.

Quantum logic inspired embedding (aka Quantum Embedding (QE)) of a Knowledge-Base (KB) was proposed recently by Garg:2019. It is claimed that the QE preserves the logical structure of the input KB given in the form of unary and binary predicates hierarchy. Such structure preservation allows one to perform Boolean logic style deductive reasoning directly over these embedding vectors. The original QE idea, however, is limited to the transductive (not inductive) setting. Moreover, the original QE scheme runs quite slow on real applications involving millions of entities. This paper alleviates both of these key limitations. We start by reformulating the original QE problem to allow for the induction. On the way, we also underscore some interesting analytic and geometric properties of the solution and leverage them to design a faster training scheme. As an application, we show that one can achieve state-of-the-art performance on the well-known NLP task of fine-grained entity type classification by using the inductive QE approach. Our training runs 9-times faster than the original QE scheme on this task.

Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs). These methods embed any given KB into a vector space by exploiting statistical similarities among its entities and predicates but without any guarantee of preserving the underlying logical structure of the KB. This, in turn, results in poor performance of logical reasoning tasks that are solved using such distributional representations. We present a novel approach called Embed2Reason (E2R) that embeds a symbolic KB into a vector space in a logical structure preserving manner. This approach is inspired by the theory of Quantum Logic. Such an embedding allows answering membership based complex logical reasoning queries with impressive accuracy improvements over popular SRL baselines.

Recent times have seen a growing demand for natural language querying (NLQ) interfaces to retrieve information from the structured data sources such as knowledge bases. Using this interface, business users can directly interact with a database without the knowledge of the query language or the data schema. Our earlier work describes a natural language query engine called ATHENA which has several shortcoming around ease of use and compatibility with data stores, formats and flows. In this demonstration paper, we present a tooling framework to address these challenges so that one can instantiate an NLQ system with utmost ease. Our framework makes it easy and practically applicable to all NLIDB scenarios involving different sources of structured data, file formats, and ontologies to enable natural language querying on top of them with minimal human configuration. We present the tool design and the solution to the challenges towards building such a system and demonstrate its applicability in the medical domain.

The ability to create and interact with large-scale domain-specific knowledge bases from unstructured/semi-structured data is the foundation for many industry-focused cognitive systems. We will demonstrate the Content Services system that provides cloud services for creating and querying high-quality domain-specific knowledge bases by analyzing and integrating multiple (un/semi)structured content sources. We will showcase an instantiation of the system for a financial domain. We will also demonstrate both cross-lingual natural language queries and programmatic API calls for interacting with this knowledge base.