Keeping up with the research literature plays an important role in the
workflow of scientists - allowing them to understand a field, formulate the
problems they focus on, and develop the solutions that they contribute, which
in turn shape the nature of the discipline. In this paper, we examine the
literature review practices of data scientists. Data science represents a field
seeing an exponential rise in papers, and increasingly drawing on and being
applied in numerous diverse disciplines. Recent efforts have seen the
development of several tools intended to help data scientists cope with a
deluge of research and coordinated efforts to develop AI tools intended to
uncover the research frontier. Despite these trends indicative of the
information overload faced by data scientists, no prior work has examined the
specific practices and challenges faced by these scientists in an
interdisciplinary field with evolving scholarly norms. In this paper, we close
this gap through a set of semi-structured interviews and think-aloud protocols
of industry and academic data scientists (N = 20). Our results while
corroborating other knowledge workers' practices uncover several novel
findings: individuals (1) are challenged in seeking and sensemaking of papers
beyond their disciplinary bubbles, (2) struggle to understand papers in the
face of missing details and mathematical content, (3) grapple with the deluge
by leveraging the knowledge context in code, blogs, and talks, and (4) lean on
their peers online and in-person. Furthermore, we outline future directions
likely to help data scientists cope with the burgeoning research literature.

本文研究数据科学家的文献综述实践，并通过 20 个数据科学家的半结构化访谈和思考协议证实，数据科学家在跨学科领域面临挑战，缺少细节和数学内容，借助代码，博客和演讲的知识背景解决文献信息过载问题和借助线上和线下同事的支持。最后，我们概述未来方向，旨在帮助数据科学家应对蓬勃发展的研究文献。

数据科学家如何审查学术文献

How Data Scientists Review the Scholarly Literature

Contextualized entity representations learned by state-of-the-art
transformer-based language models (TLMs) like BERT, GPT, T5, etc., leverage the
attention mechanism to learn the data context from training data corpus.
However, these models do not use the knowledge context. Knowledge context can
be understood as semantics about entities and their relationship with
neighboring entities in knowledge graphs. We propose a novel and effective
technique to infuse knowledge context from multiple knowledge graphs for
conceptual and ambiguous entities into TLMs during fine-tuning. It projects
knowledge graph embeddings in the homogeneous vector-space, introduces new
token-types for entities, aligns entity position ids, and a selective attention
mechanism. We take BERT as a baseline model and implement the
"Knowledge-Infused BERT" by infusing knowledge context from ConceptNet and
WordNet, which significantly outperforms BERT and other recent knowledge-aware
BERT variants like ERNIE, SenseBERT, and BERT_CS over eight different subtasks
of GLUE benchmark. The KI-BERT-base model even significantly outperforms
BERT-large for domain-specific tasks like SciTail and academic subsets of QQP,
QNLI, and MNLI.

通过对知识图谱进行多方位的知识语境注入，基于 BERT 的语言模型可以在微调期间解决概念和模糊实体的语境问题，从而在 GLUE 测试集的多个子任务中，KI-BERT-base 模型能够显著优于以 ERNIE、SenseBERT 和 BERT_CS 为代表的最新知识感知 BERT 变体，甚至比 BERT-large 模型在 SciTail 和 QQP、QNLI、MNLI 等领域特定任务方面表现更佳。