Question Answering (QA) is increasingly used by search engines to provide
results to their end-users, yet very few websites currently use QA technologies
for their search functionality. To illustrate the potential of QA technologies
for the website search practitioner, we demonstrate web searches that combine
QA over knowledge graphs and QA over free text -- each being usually tackled
separately. We also discuss the different benefits and drawbacks of both
approaches for web site searches. We use the case studies made of websites
hosted by the Wikimedia Foundation (namely Wikipedia and Wikidata). Differently
from a search engine (e.g. Google, Bing, etc), the data are indexed integrally,
i.e. we do not index only a subset, and they are indexed exclusively, i.e. we
index only data available on the corresponding website.

演示了将知识图谱和自由文本结合起来进行的问题回答（QA）网站搜索技术的潜力，并讨论了其优势和劣势。通过维基媒体基金会的网站案例研究，对与搜索引擎不同的全面且独有的索引方式进行了说明。

QAnswer：面向网站的问题回答搜索

QAnswer: Towards Question Answering Search over Websites

Question Answering (QA), as a research field, has primarily focused on either
knowledge bases (KBs) or free text as a source of knowledge. These two sources
have historically shaped the kinds of questions that are asked over these
sources, and the methods developed to answer them. In this work, we look
towards a practical use-case of QA over user-instructed knowledge that uniquely
combines elements of both structured QA over knowledge bases, and unstructured
QA over narrative, introducing the task of multi-relational QA over personal
narrative. As a first step towards this goal, we make three key contributions:
(i) we generate and release TextWorldsQA, a set of five diverse datasets, where
each dataset contains dynamic narrative that describes entities and relations
in a simulated world, paired with variably compositional questions over that
knowledge, (ii) we perform a thorough evaluation and analysis of several
state-of-the-art QA models and their variants at this task, and (iii) we
release a lightweight Python-based framework we call TextWorlds for easily
generating arbitrary additional worlds and narrative, with the goal of allowing
the community to create and share a growing collection of diverse worlds as a
test-bed for this task.

本论文探索了一种独特的用户指导的知识源，介绍了个人叙述的多关系问答。作者生成并发布了五个数据集 (TextWorldsQA)，并在该任务上对几种最先进的问答模型及其变种进行了全面的评估和分析，以及发布了一个轻量级的 Python 框架 (TextWorlds)，用于生成任意的世界和叙述。

多关系问答：基于叙述的机器阅读与推理在模拟世界中的应用

Multi-Relational Question Answering from Narratives: Machine Reading and  Reasoning in Simulated Worlds

This paper proposes a novel approach for relation extraction from free text
which is trained to jointly use information from the text and from existing
knowledge. Our model is based on two scoring functions that operate by learning
low-dimensional embeddings of words and of entities and relationships from a
knowledge base. We empirically show on New York Times articles aligned with
Freebase relations that our approach is able to efficiently use the extra
information provided by a large subset of Freebase data (4M entities, 23k
relationships) to improve over existing methods that rely on text features
alone.

本文提出一种新方法用于从自由文本中提取关系，该方法通过从知识库中学习单词和实体关系的低维嵌入来联合使用文本和现有知识信息，实验证明该方法能够有效地利用大量 Freebase 数据 (4M 实体、23k 关系) 提供的额外信息来改善仅依赖于文本特征的现有方法。