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.

本文提出了一种新颖的方法，其中使用针对特定类别问题的时间事实提取技术辅助 Knowledge Base Question Answering，以解决由于知识库不完整和实体 / 关系链接错误而无法检索所有相关事实的问题，实验结果显示，相对于传统方法，本文方法的答案准确性提高了约 30%。

从文本资源中有针对性地提取时间事实，以改善知识库上的时间问答

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

Question answering over knowledge bases (KB-QA) poses challenges in handling
complex questions that need to be decomposed into sub-questions. An important
case, addressed here, is that of temporal questions, where cues for temporal
relations need to be discovered and handled. We present TEQUILA, an enabler
method for temporal QA that can run on top of any KB-QA engine. TEQUILA has
four stages. It detects if a question has temporal intent. It decomposes and
rewrites the question into non-temporal sub-questions and temporal constraints.
Answers to sub-questions are then retrieved from the underlying KB-QA engine.
Finally, TEQUILA uses constraint reasoning on temporal intervals to compute
final answers to the full question. Comparisons against state-of-the-art
baselines show the viability of our method.

TEQUILA 是一个用于时间问答的方法，它可以在任何基于知识库的问答引擎之上运行。它通过四个阶段来检测问题是否有时间意图，将问题分解和重写成非时间子问题和时间限制，从潜在的基于知识库的问答引擎中检索出非时间子问题的答案，最终使用时间区间上的约束推理来计算整个问题的最终答案，与最先进的基准线相比，显示了我们方法的可行性。

基于知识库的时间问答系统 TEQUILA

TEQUILA: Temporal Question Answering over Knowledge Bases

This paper presents a multilayered architecture that enhances the
capabilities of current QA systems and allows different types of complex
questions or queries to be processed. The answers to these questions need to be
gathered from factual information scattered throughout different documents.
Specifically, we designed a specialized layer to process the different types of
temporal questions. Complex temporal questions are first decomposed into simple
questions, according to the temporal relations expressed in the original
question. In the same way, the answers to the resulting simple questions are
recomposed, fulfilling the temporal restrictions of the original complex
question. A novel aspect of this approach resides in the decomposition which
uses a minimal quantity of resources, with the final aim of obtaining a
portable platform that is easily extensible to other languages. In this paper
we also present a methodology for evaluation of the decomposition of the
questions as well as the ability of the implemented temporal layer to perform
at a multilingual level. The temporal layer was first performed for English,
then evaluated and compared with: a) a general purpose QA system (F-measure
65.47% for QA plus English temporal layer vs. 38.01% for the general QA
system), and b) a well-known QA system. Much better results were obtained for
temporal questions with the multilayered system. This system was therefore
extended to Spanish and very good results were again obtained in the evaluation
(F-measure 40.36% for QA plus Spanish temporal layer vs. 22.94% for the general
QA system).

本文介绍了一种多层架构，可以增强当前 QA 系统的功能，使其能够处理不同类型的复杂问题，并以多语言层面进行评估与比较。其中，我们设计了一个专用层来处理不同类型的时间问题，通过对原始问题表达的时间关系进行分解和重组，获得了更加准确的答案，并取得了显著的效果提升。