Expert search and team formation systems operate on collaboration networks,
with nodes representing individuals, labeled with their skills, and edges
denoting collaboration relationships. Given a keyword query corresponding to
the desired skills, these systems identify experts that best match the query.
However, state-of-the-art solutions to this problem lack transparency. To
address this issue, we propose ExES, a tool designed to explain expert search
and team formation systems using factual and counterfactual methods from the
field of explainable artificial intelligence (XAI). ExES uses factual
explanations to highlight important skills and collaborations, and
counterfactual explanations to suggest new skills and collaborations to
increase the likelihood of being identified as an expert. Towards a practical
deployment as an interactive explanation tool, we present and experimentally
evaluate a suite of pruning strategies to speed up the explanation search. In
many cases, our pruning strategies make ExES an order of magnitude faster than
exhaustive search, while still producing concise and actionable explanations.

ExES 是一个用于解释可解释人工智能（XAI）领域的专家搜索和团队形成系统的工具，使用事实和反事实的方法来提供透明度，对重要技能和合作关系进行解释，并指出新的技能和合作关系以增加被识别为专家的可能性，并通过一套修剪策略加速解释搜索以实现交互式的实用部署。

使用 ExES 解释专家搜索和团队组建系统

Explaining Expert Search and Team Formation Systems with ExES

To provide AI researchers with modern tools for dealing with the explosive
growth of the research literature in their field, we introduce a new platform,
AI Research Navigator, that combines classical keyword search with neural
retrieval to discover and organize relevant literature. The system provides
search at multiple levels of textual granularity, from sentences to
aggregations across documents, both in natural language and through navigation
in a domain-specific Knowledge Graph. We give an overview of the overall
architecture of the system and of the components for document analysis,
question answering, search, analytics, expert search, and recommendations.

我们提出了一种新的平台 AI Research Navigator，该平台结合了传统的关键词搜索和神经检索，可发现和组织相关文献，同时还可以提供多级文本粒度的搜索，从句子到跨文档的聚合，包括自然语言和领域特定的知识图导航。

一种新的神经搜索和洞察平台，用于浏览和组织人工智能研究

A New Neural Search and Insights Platform for Navigating and Organizing  AI Research

Expert search aims to find and rank experts based on a user's query. In
academia, retrieving experts is an efficient way to navigate through a large
amount of academic knowledge. Here, we study how different distributed
representations of academic papers (i.e. embeddings) impact academic expert
retrieval. We use the Microsoft Academic Graph dataset and experiment with
different configurations of a document-centric voting model for retrieval. In
particular, we explore the impact of the use of contextualized embeddings on
search performance. We also present results for paper embeddings that
incorporate citation information through retrofitting. Additionally,
experiments are conducted using different techniques for assigning author
weights based on author order. We observe that using contextual embeddings
produced by a transformer model trained for sentence similarity tasks produces
the most effective paper representations for document-centric expert retrieval.
However, retrofitting the paper embeddings and using elaborate author
contribution weighting strategies did not improve retrieval performance.

本研究使用 Microsoft Academic Graph 数据集，通过实验不同配置的文档中心投票模型，研究不同分布式学术文章表示形式 (即嵌入) 对学术专家检索的影响。结果表明使用用于句子相似性任务的转换器模型训练的上下文嵌入产生最有效的论文表示，然而通过 Citation retrofitting 的论文嵌入和使用详细的作者贡献权重策略并未提高检索性能。