One of the strongest signals for automated matching of knowledge graphs and
ontologies are textual concept descriptions. With the rise of transformer-based
language models, text comparison based on meaning (rather than lexical
features) is available to researchers. However, performing pairwise comparisons
of all textual descriptions of concepts in two knowledge graphs is expensive
and scales quadratically (or even worse if concepts have more than one
description). To overcome this problem, we follow a two-step approach: we first
generate matching candidates using a pre-trained sentence transformer (so
called bi-encoder). In a second step, we use fine-tuned transformer
cross-encoders to generate the best candidates. We evaluate our approach on
multiple datasets and show that it is feasible and produces competitive
results.

利用预训练的句子转换器生成匹配候选项，接着使用优化的双重编码器来生成最佳匹配项以便自动匹配知识图谱和本体论。在多个数据集上进行了验证并展示了可行性和卓越的结果。

KERMIT -- 一种基于 Transformer 的知识图谱匹配方法

KERMIT -- A Transformer-Based Approach for Knowledge Graph Matching

Peer-reviewed conferences, the main publication venues in CS, rely critically
on matching highly qualified reviewers for each paper. Because of the growing
scale of these conferences, the tight timelines on which they operate, and a
recent surge in explicitly dishonest behavior, there is now no alternative to
performing this matching in an automated way. This paper studies a novel
reviewer-paper matching approach that was recently deployed in the 35th AAAI
Conference on Artificial Intelligence (AAAI 2021), and has since been adopted
(wholly or partially) by other conferences including ICML 2022, AAAI 2022, and
IJCAI 2022. This approach has three main elements: (1) collecting and
processing input data to identify problematic matches and generate
reviewer-paper scores; (2) formulating and solving an optimization problem to
find good reviewer-paper matchings; and (3) a two-phase reviewing process that
shifts reviewing resources away from papers likely to be rejected and towards
papers closer to the decision boundary. This paper also describes an evaluation
of these innovations based on an extensive post-hoc analysis on real data --
including a comparison with the matching algorithm used in AAAI's previous
(2020) iteration -- and supplements this with additional numerical
experimentation.

本文研究了一种新颖的评审 - 文章匹配方法，该方法已被引入多个计算机科学领域的顶级会议，包括 AAAI，ICML，IJCAI 等，并通过对现实数据的广泛后期分析进行了评估，比较了其与 AAAI 2020 版本使用的匹配算法的不同之处。

大型会议上的论文与审稿人匹配

Matching Papers and Reviewers at Large Conferences

Finding the right reviewers to assess the quality of conference submissions
is a time consuming process for conference organizers. Given the importance of
this step, various automated reviewer-paper matching solutions have been
proposed to alleviate the burden. Prior approaches, including bag-of-words
models and probabilistic topic models have been inadequate to deal with the
vocabulary mismatch and partial topic overlap between a paper submission and
the reviewer's expertise. Our approach, the common topic model, jointly models
the topics common to the submission and the reviewer's profile while relying on
abstract topic vectors. Experiments and insightful evaluations on two datasets
demonstrate that the proposed method achieves consistent improvements compared
to available state-of-the-art implementations of paper-reviewer matching.

我们提出了一种基于公共主题模型的自动匹配论文与审稿人的方法，该方法可以缓解传统自动匹配方法中的词汇错配和主题重叠等问题，并通过实验验证比现有的方法更有效。