Targeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases

by   Nithish Kannen, et al.

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 λ-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 ∼30% relative improvement in answer accuracy, demonstrating the effectiveness of our approach.


Forecasting Question Answering over Temporal Knowledge Graphs

Question answering over temporal knowledge graphs (TKGQA) has recently f...

Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks

Visual Question Answering (VQA) has attracted much attention since it of...

CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases

How can we enable computers to automatically answer questions like "Who ...

Translating Place-Related Questions to GeoSPARQL Queries

Many place-related questions can only be answered by complex spatial rea...

Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge

This paper proposes a new task in the field of Answering Subjective Indu...

Database Reasoning Over Text

Neural models have shown impressive performance gains in answering queri...

TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions

We describe two new related resources that facilitate modelling of gener...

Please sign up or login with your details

Forgot password? Click here to reset