Question answering over knowledge graphs and other RDF data has been greatly
advanced, with a number of good systems providing crisp answers for natural
language questions or telegraphic queries. Some of these systems incorporate
textual sources as additional evidence for the answering process, but cannot
compute answers that are present in text alone. Conversely, systems from the IR
and NLP communities have addressed QA over text, but such systems barely
utilize semantic data and knowledge. This paper presents the first system for
complex questions that can seamlessly operate over a mixture of RDF datasets
and text corpora, or individual sources, in a unified framework. Our method,
called UNIQORN, builds a context graph on-the-fly, by retrieving
question-relevant evidences from the RDF data and/or a text corpus, using
fine-tuned BERT models. The resulting graph is typically rich but highly noisy.
UNIQORN copes with this input by a graph algorithm for Group Steiner Trees,
that identifies the best answer candidates in the context graph. Experimental
results on several benchmarks of complex questions with multiple entities and
relations, show that \uniqorn significantly outperforms state-of-the-art
methods for QA over heterogeneous sources. The graph-based methodology provides
user-interpretable evidence for the complete answering process.

UNIQORN 是一个基于图形方法的系统，它能够在混合的 RDF 数据和文本语料库以及单独的信息源中无缝地进行操作，该方法通过使用细化的 BERT 模型从 RDF 数据和文本语料库中检索与问题相关的证据来实时构建上下文图，然后使用基于图算法的 Group Steiner Trees 来确定上下文图中最佳答案候选项，实验结果表明其显著优于现有的异构数据源 QA 方法。

UNIQORN: 基于 RDF 知识图谱和自然语言文本的统一问答

UNIQORN: Unified Question Answering over RDF Knowledge Graphs and  Natural Language Text

Entity summarization is the problem of computing an optimal compact summary
for an entity by selecting a size-constrained subset of triples from RDF data.
Entity summarization supports a multiplicity of applications and has led to
fruitful research. However, there is a lack of evaluation efforts that cover
the broad spectrum of existing systems. One reason is a lack of benchmarks for
evaluation. Some benchmarks are no longer available, while others are small and
have limitations. In this paper, we create an Entity Summarization BenchMark
(ESBM) which overcomes the limitations of existing benchmarks and meets
standard desiderata for a benchmark. Using this largest available benchmark for
evaluating general-purpose entity summarizers, we perform the most extensive
experiment to date where 9~existing systems are compared. Considering that all
of these systems are unsupervised, we also implement and evaluate a supervised
learning based system for reference.

本文创建了一个最大的可用基准测试方法 (ESBM)，以评估 9 个现有系统，考虑到所有这些系统都是无监督的，我们还实施和评估了一种基于监督学习的系统。该方法能够选择 RDF 数据中的子集来计算实体的最佳紧凑摘要，支持多种应用，并且已经得到了有益的研究。

ESBM: 实体概述基准

ESBM: An Entity Summarization BenchMark

The task of answering natural language questions over RDF data has received
wide interest in recent years, in particular in the context of the series of
QALD benchmarks. The task consists of mapping a natural language question to an
executable form, e.g. SPARQL, so that answers from a given KB can be extracted.
So far, most systems proposed are i) monolingual and ii) rely on a set of
hard-coded rules to interpret questions and map them into a SPARQL query. We
present the first multilingual QALD pipeline that induces a model from training
data for mapping a natural language question into logical form as probabilistic
inference. In particular, our approach learns to map universal syntactic
dependency representations to a language-independent logical form based on
DUDES (Dependency-based Underspecified Discourse Representation Structures)
that are then mapped to a SPARQL query as a deterministic second step. Our
model builds on factor graphs that rely on features extracted from the
dependency graph and corresponding semantic representations. We rely on
approximate inference techniques, Markov Chain Monte Carlo methods in
particular, as well as Sample Rank to update parameters using a ranking
objective. Our focus lies on developing methods that overcome the lexical gap
and present a novel combination of machine translation and word embedding
approaches for this purpose. As a proof of concept for our approach, we
evaluate our approach on the QALD-6 datasets for English, German & Spanish.

研究发现，多语言自然语言问答技术仍存在困难，我们提出了一种利用 DUDES 和 MCMC 方法建立模型的 QALD 管道，成功将自然语言问题转换为可执行的 SPARQL 查询，并且在英语、德语和西班牙语环境下进行了测试。