Many decision-making scenarios, e.g., public policy, healthcare, business,
and disaster response, require accommodating the preferences of multiple
stakeholders. We offer the first formal treatment of reasoning with
multi-stakeholder qualitative preferences in a setting where stakeholders
express their preferences in a qualitative preference language, e.g., CP-net,
CI-net, TCP-net, CP-Theory. We introduce a query language for expressing
queries against such preferences over sets of outcomes that satisfy specified
criteria, e.g., $\mlangpref{\psi_1}{\psi_2}{A}$ (read loosely as the set of
outcomes satisfying $\psi_1$ that are preferred over outcomes satisfying
$\psi_2$ by a set of stakeholders $A$). Motivated by practical application
scenarios, we introduce and analyze several alternative semantics for such
queries, and examine their interrelationships. We provide a provably correct
algorithm for answering multi-stakeholder qualitative preference queries using
model checking in alternation-free $\mu$-calculus. We present experimental
results that demonstrate the feasibility of our approach.

研究了多利益相关者的定性偏好问题，并提出了针对该问题的查询语言，研究了不同语义的交互关系，并提供了一个证明正确的算法用于回答多利益相关者的定性偏好查询，通过模型检验和交替自由的 μ 算法，实验证明了该方法的可行性。

多利益相关者定性偏好查询的表示与推理

Representing and Reasoning with Multi-Stakeholder Qualitative Preference  Queries

In the evolving landscape of artificial intelligence, multimodal and
Neuro-Symbolic paradigms stand at the forefront, with a particular emphasis on
the identification and interaction with entities and their relations across
diverse modalities. Addressing the need for complex querying and interaction in
this context, we introduce SNeL (Structured Neuro-symbolic Language), a
versatile query language designed to facilitate nuanced interactions with
neural networks processing multimodal data. SNeL's expressive interface enables
the construction of intricate queries, supporting logical and arithmetic
operators, comparators, nesting, and more. This allows users to target specific
entities, specify their properties, and limit results, thereby efficiently
extracting information from a scene. By aligning high-level symbolic reasoning
with low-level neural processing, SNeL effectively bridges the Neuro-Symbolic
divide. The language's versatility extends to a variety of data types,
including images, audio, and text, making it a powerful tool for multimodal
scene understanding. Our evaluations demonstrate SNeL's potential to reshape
the way we interact with complex neural networks, underscoring its efficacy in
driving targeted information extraction and facilitating a deeper understanding
of the rich semantics encapsulated in multimodal AI models.

介绍了 SNeL（结构化神经符号语言）查询语言，结合了高级符号推理和低级神经处理，可用于从多模态数据中提取信息，评估表明它的潜力能够重塑我们与复杂神经网络交互的方式。

SNeL: 一种面向实体的多模态场景理解的结构化神经符号语言

SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal  Scene Understanding

We present a system that allows a user to search a large linguistically
annotated corpus using syntactic patterns over dependency graphs. In contrast
to previous attempts to this effect, we introduce a light-weight query language
that does not require the user to know the details of the underlying syntactic
representations, and instead to query the corpus by providing an example
sentence coupled with simple markup. Search is performed at an interactive
speed due to an efficient linguistic graph-indexing and retrieval engine. This
allows for rapid exploration, development and refinement of syntax-based
queries. We demonstrate the system using queries over two corpora: the English
wikipedia, and a collection of English pubmed abstracts. A demo of the
wikipedia system is available at: this https URL

本文提出了一种使用依存语法图的句法模式来搜索大型语言学注释语料库的系统，并介绍了一种轻量级查询语言，让用户无需了解底层语法表示的细节，而是通过提供具有简单标记的示例句子来查询。

基于示例的语法搜索

Syntactic Search by Example

SPARQL is a highly powerful query language for an ever-growing number of
Linked Data resources and Knowledge Graphs. Using it requires a certain
familiarity with the entities in the domain to be queried as well as expertise
in the language's syntax and semantics, none of which average human web users
can be assumed to possess. To overcome this limitation, automatically
translating natural language questions to SPARQL queries has been a vibrant
field of research. However, to this date, the vast success of deep learning
methods has not yet been fully propagated to this research problem. This paper
contributes to filling this gap by evaluating the utilization of eight
different Neural Machine Translation (NMT) models for the task of translating
from natural language to the structured query language SPARQL. While
highlighting the importance of high-quantity and high-quality datasets, the
results show a dominance of a CNN-based architecture with a BLEU score of up to
98 and accuracy of up to 94%.

本篇论文采用八种不同的神经机器翻译模型，探讨将自然语言问题翻译成结构化查询语言 SPARQL 的方法，结果表明基于 CNN 的架构效果最佳， BLEU 分数高达 98，准确率高达 94%。