We address the challenges and opportunities in the development of knowledge
systems for Sanskrit, with a focus on question answering. By proposing a
framework for the automated construction of knowledge graphs, introducing
annotation tools for ontology-driven and general-purpose tasks, and offering a
diverse collection of web-interfaces, tools, and software libraries, we have
made significant contributions to the field of computational Sanskrit. These
contributions not only enhance the accessibility and accuracy of Sanskrit text
analysis but also pave the way for further advancements in knowledge
representation and language processing. Ultimately, this research contributes
to the preservation, understanding, and utilization of the rich linguistic
information embodied in Sanskrit texts.

通过自动构建知识图谱的框架、引入本体驱动和通用任务的注释工具，以及提供多样化的网页界面、工具和软件库，我们在梵语的知识系统发展中解决了挑战与机遇，尤其在问答方面。这些贡献不仅提高了梵语文本分析的可访问性和准确性，而且为知识表达和语言处理领域的进一步发展铺平了道路。最终，这项研究对于保护、理解和利用梵语文本中所蕴含的丰富语言信息具有重要意义。

基于梵语知识的系统：注释和计算工具

Sanskrit Knowledge-based Systems: Annotation and Computational Tools

Taxonomies, which organize domain concepts into hierarchical structures, are
crucial for building knowledge systems and downstream applications. As domain
knowledge evolves, taxonomies need to be continuously updated to include new
concepts. Previous approaches have mainly focused on adding concepts to the
leaf nodes of the existing hierarchical tree, which does not fully utilize the
taxonomy's knowledge and is unable to update the original taxonomy structure
(usually involving non-leaf nodes). In this paper, we propose a two-stage
method called ATTEMPT for taxonomy completion. Our method inserts new concepts
into the correct position by finding a parent node and labeling child nodes.
Specifically, by combining local nodes with prompts to generate natural
sentences, we take advantage of pre-trained language models for
hypernym/hyponymy recognition. Experimental results on two public datasets
(including six domains) show that ATTEMPT performs best on both taxonomy
completion and extension tasks, surpassing existing methods.

本文提出了名为 ATTEMPT 的两阶段方法，通过找到父节点并标记子节点的方式，将新概念插入正确的位置。利用预训练的语言模型进行上下位关系识别，通过合并本地节点和提示生成自然语句。实验结果表明，ATTEMPT 在分类系统补全和扩展任务上表现最佳，超过了现有方法。

找出父级，然后标记子级：使用预训练语言模型的两阶段分类法完成方法

Find Parent then Label Children: A Two-stage Taxonomy Completion Method  with Pre-trained Language Model

Knowledge can't be disentangled from people. As AI knowledge systems mine
vast volumes of work-related data, the knowledge that's being extracted and
surfaced is intrinsically linked to the people who create and use it. When
these systems get embedded in organizational settings, the information that is
brought to the foreground and the information that's pushed to the periphery
can influence how individuals see each other and how they see themselves at
work. In this paper, we present the looking-glass metaphor and use it to
conceptualize AI knowledge systems as systems that reflect and distort,
expanding our view on transparency requirements, implications and challenges.
We formulate transparency as a key mediator in shaping different ways of
seeing, including seeing into the system, which unveils its capabilities,
limitations and behavior, and seeing through the system, which shapes workers'
perceptions of their own contributions and others within the organization.
Recognizing the sociotechnical nature of these systems, we identify three
transparency dimensions necessary to realize the value of AI knowledge systems,
namely system transparency, procedural transparency and transparency of
outcomes. We discuss key challenges hindering the implementation of these forms
of transparency, bringing to light the wider sociotechnical gap and
highlighting directions for future Computer-supported Cooperative Work (CSCW)
research.

人工智能知识系统与透明度的挑战，包括知识与人的关联性，对组织和个体视野的影响，以及系统透明度、程序透明度和结果透明度等方面，需要引起进一步研究和关注。